Boundary Region Research Articles

A Spiral Coverage Path Planning Algorithm for Nonomnidirectional Robots

ABSTRACTThe limited steering capabilities of nonomnidirectional robots introduce significant complexity into complete coverage tasks, often leading to increased path overlap or incomplete coverage of certain areas. Although recent research has made progress in optimizing coverage path planning, redundant coverage or omissions are still prone to occur in the target area to be covered. To address these persistent challenges, we propose a novel spiral coverage method. This approach not only conforms to the kinematic constraints of nonomnidirectional robots but also enhances coverage efficiency by dividing the target area into center and boundary regions and devising tailored coverage strategies for each. This method effectively reduces path redundancy and improves overall area coverage. Furthermore, we introduce a comprehensive metric that combines total path length and area coverage ratio to evaluate coverage efficiency, overcoming the limitations and computational complexity associated with existing metrics. For scenarios where maximizing the area coverage ratio is critical, we have developed a high‐coverage‐rate turning strategy that ensures 100% coverage. Through simulation tests in six representative areas and actual experiments on airport runways, our method shows an improvement of 0.238%–14.538% in coverage efficiency compared with parallel coverage method and 60.548%–76.339% compared with deep reinforcement learning‐based method. Additionally, implementing high‐coverage‐rate turning strategies improves the area coverage ratio by 2.021%–6.732%. In field experiments, our method reduces execution time by 1.61% compared with parallel coverage method. These results show that our method has a significant effect in improving coverage efficiency and achieving complete coverage goals.

Unintended consequences: China's pairing assistance policy and carbon emissions in administrative border areas -evidence from China.

The investigation of the unintended impact of pairing assistance policies on carbon emissions in administrative boundary regions is critical for achieving the "dual carbon" goals. This paper utilizes a sample of cities from the Pearl River Delta and the eastern and western regions of Guangdong, China, spanning from 2006 to 2020. A quasi-natural experiment based on the co-construction of industrial parks is employed to examine its impact on carbon emissions in boundary regions. The study finds that while pairing assistance policies are intended to suppress carbon emissions in assisted boundary regions, they inadvertently exacerbate carbon emissions in those regions. This effect is realized by breaking market segmentation and forcing high-carbon enterprises to exit. The increase in carbon emissions in assisted boundary regions is driven by the industrial transfer mechanism, with regions having lower labor costs more likely to attract polluting enterprises. Further analysis reveals a "distance attenuation effect" of the policy on carbon emissions in the assisted regions. Directive assistance relationships from upper-level governments, moderate environmental regulation, and high levels of economic development can amplify the carbon reduction effect of the policy in boundary regions. This study contributes to a deeper understanding of the causes of persistently high carbon emissions in boundary regions and provides theoretical insights for developing countries to better coordinate interregional assistance policies and strengthen environmental governance.

A Coarse-to-Fine Feature Aggregation Neural Network with a Boundary-Aware Module for Accurate Food Recognition

Food recognition from images is crucial for dietary management, enabling applications like automated meal tracking and personalized nutrition planning. However, challenges such as background noise disrupting intra-class consistency, inter-class distinction, and domain shifts due to variations in capture angles, lighting, and image resolution persist. This study proposes a multi-stage convolutional neural network-based framework incorporating a boundary-aware module (BAM) for boundary region perception, deformable ROI pooling (DRP) for spatial feature refinement, a transformer encoder for capturing global contextual relationships, and a NetRVLAD module for robust feature aggregation. The framework achieved state-of-the-art performance on three benchmark datasets, with Top-1 accuracies of 99.80% on the Food-5k dataset, 99.17% on the Food-101 dataset, and 85.87% on the Food-2k dataset, significantly outperforming existing methods. This framework holds promise as a foundational tool for intelligent dietary management, offering robust and accurate solutions for real-world applications.

Adaptive ensemble loss and multi-scale attention in breast ultrasound segmentation with UMA-Net.

The generalization of deep learning (DL) models is critical for accurate lesion segmentation in breast ultrasound (BUS) images. Traditional DL models often struggle to generalize well due to the high frequency and scale variations inherent in BUS images. Moreover, conventional loss functions used in these models frequently result in imbalanced optimization, either prioritizing region overlap or boundary accuracy, which leads to suboptimal segmentation performance. To address these issues, we propose UMA-Net, an enhanced UNet architecture specifically designed for BUS image segmentation. UMA-Net integrates residual connections, attention mechanisms, and a bottleneck with atrous convolutions to effectively capture multi-scale contextual information without compromising spatial resolution. Additionally, we introduce an adaptive ensemble loss function that dynamically balances the contributions of different loss components during training, ensuring optimization across key segmentation metrics. This novel approach mitigates the imbalances found in conventional loss functions. We validate UMA-Net on five diverse BUS datasets-BUET, BUSI, Mendeley, OMI, and UDIAT-demonstrating superior performance. Our findings highlight the importance of addressing frequency and scale variations, confirming UMA-Net as a robust and generalizable solution for BUS image segmentation.

Loops and the geometry of chance

AbstractSuppose your evil sibling travels back in time, intending to lethally poison your grandfather during his infancy. Determined to save grandpa, you grab two antidotes and follow your sibling through the wormhole. Under normal circumstances, each antidote has a 50% chance of curing a poisoning. Upon finding young grandpa, poisoned, you administer the first antidote. Alas, it has no effect. The second antidote is your last hope. You administer it—and success: the paleness vanishes from grandpa's face, he is healed. As you administered the first antidote, what was the chance that it would be effective? This essay offers a systematic account of this case, and others like it. The central question is this: Given a certain time travel structure, what are the chances? In particular, I'll develop a theory about the connection between these chances and the chances in ordinary, time‐travel‐free contexts. Central to the account is a Markov condition involving the boundaries of spacetime regions.

Quantum Broadcast Channel Simulation via Multipartite Convex Splitting

We show that the communication cost of quantum broadcast channel simulation under free entanglement assistance between the sender and the receivers is asymptotically characterized by an efficiently computable single-letter formula in terms of the channel’s multipartite mutual information. Our core contribution is a new one-shot achievability result for multipartite quantum state splitting via multipartite convex splitting. As part of this, we face a general instance of the quantum joint typicality problem with arbitrarily overlapping marginals. The crucial technical ingredient to sidestep this difficulty is a conceptually novel multipartite mean-zero decomposition lemma, together with employing recently introduced complex interpolation techniques for sandwiched Rényi divergences. Moreover, we establish an exponential convergence of the simulation error when the communication costs are within the interior of the capacity region. As the costs approach the boundary of the capacity region moderately quickly, we show that the error still vanishes asymptotically.

Enhanced flexoelectric properties in sodium bismuth titanate-based lead-free relaxor ferroelectrics by inducing polar nanoregions

The presence of polar nanoregions (PNRs) is widely recognized as playing a crucial role in the functionalities of numerous solid dielectrics. However, the contribution of PNRs to the specific coupling between strain gradient and electric polarization (flexoelectricity) has not yet been thoroughly understood. Herein, we investigated the impact of varying PNR concentrations on the dielectric and flexoelectric properties of lead-free relaxor ferroelectric (Bi0.5Na0.5)TiO3 ceramics, achieved through stoichiometric BaTiO3 doping. The temperature-dependent dielectric properties indicated the presence of PNRs in doubly doped ceramics as compared to pure BNT. Complex impedance analysis and piezoresponse force microscopy measurements demonstrated significant differences in PNR concentrations among the different components. The reorientation of PNRs notably enhanced the flexoelectric response of the doubly doped BNT-xBT ceramics, with this enhancement being positively correlated with the concentration of PNR. Specifically, the flexoelectric coefficient was found to be approximately 10 times higher in the morphotropic phase boundary (MPB) region (x = 0.08) compared to pure BNT. These findings illustrate that adjusting the concentration of PNRs in lead-free relaxation ferroelectrics is an effective strategy for improving flexoelectric properties.

Morphotropic Phase Boundary Region 0.7BiFeO3-0.3BaTiO3 Ceramics Exploration Under the Influence of the Incorporated Sn-Ions for Piezo/Ferro Applications

In the field of piezoelectric applications, perovskite-based multifunctional composite ceramics are widely explored. The morphotropic phase boundary (MPB) regions, where dual structural phases coexist, play a crucial role in boosting the ferroelectric and piezoelectric properties significantly. Herein, MPB-region-existent 0.7BiFeO3-0.3BaTiO3 (BFBT) composite ceramic is investigated under the influence of wt%Sn-ion incorporation at the lattice sites of the BFBT. Specifically, the ceramic composition BFBT:0.2Sn has demonstrated excellent remnant polarization (Pr ~ 22.68 µC/cm2), an impressive piezoelectric coefficient (d33 ~ 211 pC/N), stable impedance of 1.07 × 107 Ω, a Curie temperature of 435 °C and low dielectric loss (tanδ) of <0.5. Moreover, the BFBT:0.2Sn ceramic has also maintained a stable d33 of ~150 pC/N and resistivity of ~102 Ω even at a temperature of 400 °C. Such outcomes confirm the ability and potential of the BFBT:0.2Sn ceramic composition for high-temperature piezoelectric applications.

AFN-Net: Adaptive Fusion Nucleus Segmentation Network Based on Multi-Level U-Net.

The task of nucleus segmentation plays an important role in medical image analysis. However, due to the challenge of detecting small targets and complex boundaries in datasets, traditional methods often fail to achieve satisfactory results. Therefore, a novel nucleus segmentation method based on the U-Net architecture is proposed to overcome this issue. Firstly, we introduce a Weighted Feature Enhancement Unit (WFEU) in the encoder decoder fusion stage of U-Net. By assigning learnable weights to different feature maps, the network can adaptively enhance key features and suppress irrelevant or secondary features, thus maintaining high-precision segmentation performance in complex backgrounds. In addition, to further improve the performance of the network under different resolution features, we designed a Double-Stage Channel Optimization Module (DSCOM) in the first two layers of the model. This DSCOM effectively preserves high-resolution information and improves the segmentation accuracy of small targets and boundary regions through multi-level convolution operations and channel optimization. Finally, we proposed an Adaptive Fusion Loss Module (AFLM) that effectively balances different lossy targets by dynamically adjusting weights, thereby further improving the model's performance in segmentation region consistency and boundary accuracy while maintaining classification accuracy. The experimental results on 2018 Data Science Bowl demonstrate that, compared to state-of-the-art segmentation models, our method shows significant advantages in multiple key metrics. Specifically, our model achieved an IOU score of 0.8660 and a Dice score of 0.9216, with a model parameter size of only 7.81 M. These results illustrate that the method proposed in this paper not only excels in the segmentation of complex shapes and small targets but also significantly enhances overall performance at lower computational costs. This research offers new insights and references for model design in future medical image segmentation tasks.

Optimizing convex hull discovery: Introducing a quintuple-region algorithm with enhanced computational efficiency

This paper introduces a novel algorithm for computing the convex hull of a finite set of points in two-dimensional space. Unlike traditional methods, this algorithm strategically partitions the input set into five distinct regions, isolating interior points to reduce computational efforts while determining the convex hulls of the four boundary regions. This innovative approach significantly diminishes the required number of operations, achieving a computational complexity of O(nk), where k denotes the number of vertices on the convex hull and n is the number of vertices. The study methodically elaborates on the development of this algorithm, starting with a comprehensive overview of convex hull-related terminology and existing methodologies. Subsequent sections detail the process of identifying endpoints within the set, the division of the set into specific regions, and the application of proposed algorithms for each region to efficiently compute their convex hulls. Through theoretical analysis and case studies presented in the latter sections, this paper not only enhances understanding of convex hull computation but also demonstrates the practical efficiency and effectiveness of the proposed algorithm in various scenarios. This advancement represents a significant contribution to the field of computational geometry, offering improved solutions for problems involving two-dimensional spatial analysis.

Water protection zones and riparian buffers within the Northern Development Region of the Republic of Moldova

The present research is dedicated to the delimitation of water protection zones and riparian buffers for the protection of the river banks and reservoirs built on their course within the boundaries of the Northern Development Region of the Republic of Moldova. The assessment of the spatial distribution of these areas was carried out on the basis of national legislation using geoinformational techniques. The total area of the protection zones was calculated at 2767 km2. Referring to the river type, about 14.7% of the delimited area is estimated for large rivers, 16.2% for medium rivers, 12.6% for small rivers, 52.6% for very small rivers and 3.9% for streams. The area of riparian buffers reaches 306 km2, which represents about 11% of the area of the water protection zones. The land cover within the boundaries of the Prut River’s riparian buffers is characterized by the highest share of forests, 78%. In the case of the Nistru River, these areas are divided between 28% arable land, 20% forest, 17% grassland.

Turbulent flow pattern and flow resistance characteristics of cross flow over square-arranged tube bundles with different pitch to diameter ratios

Helical tube bundles are used in steam generators and intermediate heat exchangers of high temperature gas-cooled reactors due to their compactness and good thermal expansion adaptation performance. However, the characteristics of cross flow over inline tube bundles are sensitive to pitch to diameter ratios (S/D) and Reynolds numbers (Re), and the influence of S/D and Re variation on the flow patterns (especially wake patterns) and pressure drop coefficients (ξ) remains unclear. In the current investigation, the flow characteristics of tube bundles with S/D = 1.58 and 1.88 with various Re are investigated with the aid of wind tunnel experiments and large eddy simulations (LES). For the tube bundle with S/D = 1.88, the results of LES with periodical geometry align well with wind tunnel experiments, successfully predicting the decreasing recirculation region size as Re increases. In contrast, for the tube bundle with S/D = 1.58, the recirculation region size remains almost constant with varying Re, filling the space between two adjacent tubes even when Re = 36 000. However, the LES with periodical geometry only successfully predicts flow phenomena up to Re = 25 285. The wakes become oblique when Re = 36 000, which differs from experiments. Based on the force balance on the recirculation region, the pressure drop coefficient (ξ) of the cross flow over tube bundles is related to the size of the recirculation region. For the tube bundle with S/D = 1.88, the recirculation region size decreases as Re increases when Re ≥ 18 000, resulting in a larger flow cross section area in the main flow region. The increasing main flow cross section area leads to an increased pressure coefficient along the separation streamlines. Since the Reynolds normal and shear stresses cancel each other out, the increasing pressure coefficients along the recirculation region boundary lead to the increasing surface pressure coefficients in the rear side of the tube and ultimately lead to the ξ decrease from 0.26 to 0.24 when Re increases from 18 000 to 44 571. Conversely, when Re ≤ 10 285, the recirculation area fills the space between two adjacent tubes, so the ξ barely change with Re. For tube bundle with S/D = 1.58, the recirculation area fills the space between tubes even when Re ≥ 18 000, leading to almost the unchanged ξ (≈0.27) with Re. The higher ξ at Re = 10 285 is related to the bounding wall effects on the near wall tubes.

Conductive and convective combustion modes of granular mixtures of Ti–C–NiCr

The combustion modes of powder and granular mixtures (100 – X)(Ti + C) + XNiCr (X = 0–30%) containing Ti powders of different dispersion with different amounts of impurity gases in them were investigated. The experimental setup provided filtration of impurity gases released during combustion in the cocurrent direction or through the side surface of the sample. The difference between the experimental burning velocities of powder mixtures with titanium of different fineness is explained using a convective-conductive combustion model. For granular mixtures based on Ti powder with a characteristic size of 120 μm, it was shown that combustion occurs in the conductive mode. Comparison of the combustion velocities of granular mixtures containing Ti powder with particles of a characteristic size of 60 μm in the absence and presence of gas filtration through the sample indicates the transition of combustion to the convective regime. The necessary and sufficient conditions for the transition from conductive to convective combustion are formulated, which makes it possible to determine the composition of the mixture whose combustion occurs in the boundary region. In mixtures based on Ti with a particle size of 60 μm, the conductive combustion regime is observed during the combustion of granules 0.6 mm in size and a mixture with X = 30% of granules 1.7 mm in size. For mixtures with X = 0–20% with granules 1.7 mm in size, burning in the convective regime, the interfacial heat transfer coefficients were evaluated using experimental data. Their values are more than an order of magnitude higher than the theoretical ones. The XPA results of the combustion products showed that in order to obtain synthesis products without side phases of intermetallic compounds, it is necessary to use finely dispersed titanium powder.

Observation and Control of Single-Component Adhesion Interphase of Polyamide 66 through Confocal Raman Microspectroscopy.

Manufacturing using adhesion technology has attracted much attention. Examples of adhesion include the lay-up of carbon fiber reinforced thermoplastic prepregs and the lamination of food packaging. In single-component adhesion systems, the analysis of the boundary region poses challenges because of the absence of chemical and physical discrimination at the adhesion interphase. Polyamide 66, one of the typical engineering plastics, is widely accepted as a structural material in automobiles and packaging films. Therefore, finer control of adhesion with polyamide 66 is crucial for advancing adhesion manufacturing. In this work, we focused and investigated the interphase of a single-component adhesion system with polyamide 66. For the analyses of single-component polyamide 66 laminates, an adhesion system with nondeuterated and deuterated polyamides was utilized, and their interphase structures were evaluated by confocal Raman microspectroscopy. The interphase region of the adhesion specimens was able to be characterized and evaluated, revealing an expansion to a thickness of several micrometers. The interphase thickness was increased with thermal annealing postlamination, whereas no thickness increase was observed in adhered specimens using the polyamide 66 substrates through thermal crystallization before lamination. The formation of the interphase region can be attributed to the crystal growing and lamella interlocking in the boundary region. Moreover, the larger interphase thickness was strongly associated with an increase in adhesion fracture toughness. These results suggested that the adhesion properties of crystalline substrates were decided by crystallization behavior and the thermal annealing process, even when using the same component adhesion systems.

Лесные ресурсы горного региона: потенциал и риски развития (на материалах Республики Тыва)

Today the study and analysis of the conditions of forest resources, its potential and development risks in such mountainous and border areas of the country as the Republic of Tyva has become most relevant. It has both theoretical and practical significance. The paper presents materials on the condition of forest resources of the mountainous region, its potential and development risks within the boundary and depressive regional subject of Russia, i.e. the Republic of Tyva. Forest resources, in addition to their economic and recreational function also perform soil-protective, water-protective and climate-regulating functions, and they become a basis for creation of manufacturing and processing industry in this region. But their use for the social and economic development of the republic remains at a low level. Moreover, the forest complex annually suffers from forest fires and biological pests. It has been revealed that the main problems and risks of the forest complex development in this region are forest fires, anthropogenic phenomena, mountainous territory, its deep continentality, lack of appropriate infrastructure as well as its natural and climatic conditions. We offer the following measures for solving the existing problems and risks of the forest complex development in the Republic of Tyva: (1) strengthening the work on monitoring, forecasting and prevention of the most dangerous risks of forest resources development in the region, such as forest fires, invasion of biological pests, etc.; (2) development of proposals to stimulate demand for forest resources in a mountainous boundary region of the country.

Efficient knowledge updating method for inconsistent decision tables

With the continuous expansion of data from different domains, there are lots of inconsistent data in decision tables. Approximations are fundamental notions of rough set theory. However, inconsistent decision tables (IDTs) may update with new coming objects due to the information collection and update. In this circumstance, how to dynamically acquire useful approximations with previous knowledge in IDTs is crucial for data analysis and knowledge discovery. To address this issue, we focus on exploiting incremental approaches for computing approximations when objects are changing in IDTs over time, which is of great significance for gaining decision rules in the context of dynamic environments. First, we introduce the concepts of inconsistent decision tables and approximations. Furthermore, with dynamic changing objects, several principles of updating approximations are investigated according to the positive and boundary regions. Following the proposed principles, incremental algorithms for acquiring approximations in IDTs are presented while the objects change with time. In the end, comparative results are illustrated to verify effectiveness of updating lower and upper approximations in IDTs, displaying the advantage of the proposed approaches.

Unveiling and mapping polymorphs in fluorite Y2TiO5 using 4D‐STEM and unsupervised machine learning

AbstractY2TiO5 belongs to the Ln2TiO5 (Ln = lanthanide or Y) family of ceramic materials and exhibits a range of desirable material properties such as radiation tolerance, frustrated magnetism, and large dielectric constant. However, understanding the complex crystal structure of Y2TiO5 remains elusive, given that Y2TiO5 can adopt multiple polymorphs such as cubic, orthorhombic, and hexagonal phases within the lattice. In this work, we report a detailed structural analysis of Y2TiO5 using four‐dimensional scanning transmission electron microscopy coupled with unsupervised machine learning. The pyrochlore nanodomains, characterized by the ordered arrangement of yttrium cations on the A site of their A2BO5 structure, are present within the matrix of a predominantly fluorite‐structured Y2TiO5 along with a third polymorph, the hexagonal phase. The pyrochlore phase is found to form 2 nm boundary regions around hexagonal phase stacking faults, highlighting the potential influence of the hexagonal phase on the occurrence and distribution of the pyrochlore phase. Lastly, we identify a unique pyrochlore phase with asymmetric arrangement of cation ordering along a single planar direction. Our findings provide invaluable insights into the possible mechanisms stabilizing pyrochlore nanodomains within the fluorite lattice of Y2TiO5.

SEISMICITY of the ARCTIC in 2020

The article provides an overview and analysis of seismicity within the boundaries of the Arctic region for 2020 based on a consolidated catalog of earthquakes compiled from catalogs of the N. Laverov Federal Center for Integrated Arctic Research of the Ural Branch of the Russian Academy of Sciences, the Kola and Ya kutsk branches of the Geophysical Survey of the Russia Academy of Sciences (GS RAS), using data from the Seismological Bulletin of the GS RAS and the International Seismological Center. A total of 475 earthquakes are included in the consolidated catalog for 2020. Most of the earthquakes that occurred in 2020, including all the strongest earthquakes, were located within the mid-ocean ridges of Mon, Knipovich. Gakkel and Spitsbergen Fault Zone. In the offshore territories, most of the earthquakes were confined to the Svalbard archipelago, in particular, to the seismically active zone in the Sturfjord strait. Within the shelf areas, seismicity is also characteristic of the Novaya Zemlya archipelago, "continent-ocean" transition zone of the Barents-Kara region, Bely (Kvitøya) and Bear (Bjørnøya) Islands. For 17 earthquakes, the focal mechanism parameters are presented according to the Global CMT catalog.

Thermomigration Microstructure and Properties of Ni Nanoparticle-Reinforced Sn58Bi Composite Solder/Cu Solder Joint

A Sn58Bi composite solder reinforced by Ni nanoparticles was prepared using a mechanical mixing technique, and the thermomigration microstructure and properties of the solder joints were studied. The findings indicate that incorporating an appropriate quantity of Ni nanoparticles can enhance the microstructure of the composite solder and mitigate the coarsening of Bi-phase segregation. At 0.75 weight percent Ni nanoparticle content, the composite solder’s tensile strength is 59.7 MPa and its elongation is 54.6%, both of which are noticeably greater than those of the base solder. When the thermal loading time is 576 h, the shear strength of the composite solder joint is 25.5 MPa, which is 30.1% higher than that of the base solder joint. This study reveals that the shear fracture path shifts from the boundary region between the solder seam and the IMC layer to the IMC layer itself. Concurrently, the fracture mode evolves from a mix of brittle–ductile fracture, characterized by quasi-cleavage, to a predominantly brittle fracture, marked by numerous “rock candy-like” cross-sectional features and secondary cracking. Adding Ni nanoparticles to the Sn58Bi composite solder/Cu solder junction can significantly extend its service life.

Effect of Temperature Gradient and Cooling Rate on the Solidification of Iron: A Molecular Dynamics Study

In this study, molecular dynamics (MD) simulations were employed to compare the effects of different solidification conditions on the solidification behaviour, stress distribution, and degree of crystallization of iron. The results indicate significant differences in nucleation and microstructural evolution between the two solidification methods. In the homogeneous temperature field, the solidification of iron is characterized by instantaneous nucleation. The BCC phase surged at 1431 K followed by the phenomenon of latent heat of crystallization. As the temperature continued to decrease, the percentage of the BCC phase continued to increase steadily. Eventually, the atoms aggregated to form a crystal nucleus and grow outward to form polycrystalline structures. During gradient solidification, continuous nucleation of iron leads to a slow increase in the BCC phase. From the initial stage of solidification, the solid–liquid interface moves in the direction of higher temperature and is accompanied by a higher stress distribution. Furthermore, increasing the temperature gradient, particularly the cooling rate, accelerates the transformation efficiency of iron in the gradient solidification process. In addition, increasing the cooling rate or temperature gradient reduces the residual stress and crystallinity of the solidified microstructure. It is worth noting that an increased temperature gradient or cooling rate will produce higher residual stress and uneven microstructure in the boundary region. This study provides an atomic-level understanding of the improvement in the solidification performance of iron.