Space Scales Research Articles

A multiple scale spaces empowered approach for welding radiographic image defect segmentation

Welding radiographic image defect segmentation (WRIDS) is a key technology to promote the automation and standardization of quality inspection. However, the complexity of scale variability, aggregation and contextual relationships presented by welding defects pose a great challenge to WRIDS, such as porosity, slag and lack of penetration. To address the issue, a multiple scale spaces (MSSs) empowered segmentation method of complex welding defects is proposed. First, a multi-scale feature space is constructed by dilated convolution with different dilated rates. Second, a multi-scale semantic space is constructed by max pooling in different windows. Third, a multi-scale relational space is constructed through a self-attention mechanism. Finally, the proposed MSSs method is validated on the basis of own collected weld inspection data from an aerospace structural component. The results show that the proposed method can effectively segment multiple complex defect types with an average Acc of 99.46% and an average Miou of 74.65% on the test set.

Implementation and Assurance Model Construction of Financial BPO Cost Control under Cloud Computing Platform

Abstract Relying on their low-cost comparative advantage, Chinese enterprises have rapidly integrated into the global industrial division of the labor system, entering the fast lane of development and becoming an important driving force behind China’s rapid economic growth. Furthermore, in recent years, this situation is changing. The cost of financing for Chinese companies has been rising yearly, a trend that has become increasingly evident, especially after the new crown epidemic. Investment growth in China’s capital markets has declined, the market has seen back-to-back years of decline, and competition has become more intense. It is imperative that enterprises need to strengthen their financial cost control management to enhance their competitiveness in the market by improving cost management. Cloud computing is a new model that relies on the Internet, with a certain amount of network storage space, scalability, and fast scaling. The development of cloud computing provides new ideas for target BPO cost management. With cloud computing, target BPO cost management becomes easier, making it a market-oriented cost control strategy for enterprises, especially those in a competitive market environment. Target BPO cost management can cover the whole process from financing to revenue through the decomposition and control of cost control objectives in various aspects. The ultimate effect is to improve the cost management level and enhance enterprise management’s efficiency. Based on the theories related to target BPO cost management at home and abroad, this paper takes 6201 listed companies in China as the object of research on cost control. The three hypotheses that financial BPO cost mitigates the company’s financial risk, financial BPO control reduces the company’s financial cost and improves the revenue of listed companies are verified by establishing a financial cost mediation effect model through nine variable control indicators. The research results show that the regression coefficient of BPO cost control construction on the revenue input of listed companies is 0.67, which obviously and effectively improves the company’s revenue ( Rd ) 13.21%; the regression coefficient of BPO cost control on financial risk (Fi) is -0.55, which reduces the financial risk of listed companies by 25.6%; the research demonstrates the necessity and feasibility of applying target BPO financial cost management for listed companies The study demonstrated the necessity and feasibility of applying target BPO financial cost management to listed companies, guaranteed the implementation of the target BPO cost management method to the ground, put forward relevant safeguard measures and suggestions for possible problems in the current application of target cost management, fundamentally solved the problem of enterprise financial cost control, and at the same time provided a guiding reference for the application of target BPO cost management in SMEs.

Convergence of mechanistic modeling and artificial intelligence in hydrologic science and engineering

Hydrology is a mature physical science based on application of first principles. However, the water system is complex and its study requires analysis of increasingly large data available from conventional and novel remote sensing and IoT sensor technologies. New data-driven approaches like Artificial Intelligence (AI) and Machine Learning (ML) are attracting much “hype” despite their apparent limitations (transparency, interpretability, ethics). Some AI/ML applications lack in addressing explicitly important hydrological questions, focusing mainly on “black-box” prediction without providing mechanistic insights. We present a typology of four main types of hydrological problems based on their dominant space and time scales, review their current tools and challenges, and identify important opportunities for AI/ML in hydrology around three main topics: data management, insights and knowledge extraction, and modelling structure. Instead of just for prediction, we propose that AI/ML can be a powerful inductive and exploratory dimension-reduction tool within the rich hydrological toolchest to support the development of new theories that address standing gaps in changing hydrological systems. AI/ML can incorporate other forms of structured and non-structured data and traditional knowledge typically not considered in process-based models. This can help us further advance process-based understanding, forecasting and management of hydrological systems, particularly at larger integrated system scales with big models. We call for reimagining the original definition of AI in hydrology to incorporate not only today’s main focus on learning, but on decision analytics and action rules, and on development of autonomous machines in a continuous cycle of learning and refinement in the context of strong ethical, legal, social, and economic constrains. For this, transdisciplinary communities of knowledge and practice will need to be forged with strong investment from the public sector and private engagement to protect water as a common good under accelerated demand and environmental change.

A Critical Review on CT Image denoising in Wavelet domain

Medical images are commonly used today by medical practitioners for the purposes of diagnosis. For the purposes of diagnosis, diagnostic images are widely used today by medical professionals. In general, MRI works on soft tissues, and CT works on hard tissues. Due to device and hardware limitations, mathematical calculations, transition mechanisms in computers, there are chances of creating noise in medical images.In yhis paper, a critical review on CT image denoising has been performed in wavelet domain. In the transform domain, the process of removing noise from an image starts with the image or data being divided up into a representation in scale space. It has methods for setting thresholds, rules for shrinking, and a way to clean up noise based on wavelets, among other things.

Natural Denudation Versus Anthropogenically Accelerated Erosion in Central Brazil: A Confrontation of Time and Space Scales

AbstractLand degradation resulting from increased continental surface erosion is a worldwide and systemic phenomenon due to unsustainable human activities. Already fragile, the intertropical zone is likely to be further affected by climate change like increased aridity, an aggravating factor of soil erosion and land degradation. A major challenge is thus to provide the necessary knowledge to not only deepen our understanding of the Earth's system and its critical thresholds but also to help achieving sustainability. Understanding the factors that control the properties and processes of the critical zone, and especially what will be its responses to ongoing climate and land use changes, requires multidisciplinary efforts to tackle time scales that are compatible with morphogenesis and soil development as well as environmental disturbances of anthropogenic origin. Due to its prominent ecological importance, the Brazilian Cerrado biome is an ideal natural laboratory were to gauge the consequences of recent and intense agricultural activities on continental surface erosion. We focused on the region of Brasília where our approach allows confronting the temporal scales of long‐lived and stable cosmogenic nuclides with that of short‐lived radioactive isotopes, through a comparison of natural and anthropogenically disturbed land surfaces. Our results indicate that long‐term, background denudation rates are lower than 10 mm Kyr−1 whereas recent erosion rates due to human activities may reach rates at least 160 times higher, exceeding by far the sustainability of the soil resource.

Power variations in fractional Sobolev spaces for a class of parabolic stochastic PDEs

We consider a class of parabolic stochastic PDEs on bounded domains $D\subseteq\mathbb{R}^d$ that includes the stochastic heat equation, but with a fractional power $\gamma$ of the Laplacian. Viewing the solution as a process with values in a scale of fractional Sobolev spaces $H_r$, with $r < \gamma - d/2$, we study its power variations in $H_r$ along regular partitions of the time-axis. As the mesh size tends to zero, we find a phase transition at $r=-d/2$: the solutions have a nontrivial quadratic variation when $r<-d/2$ and a nontrivial $p$th order variation for $p= 2\gamma/(\gamma-d/2-r)>2$ when $r>-d/2$. More generally, suitably normalized power variations of any order satisfy a genuine law of large numbers in the first case and a degenerate limit theorem in the second case. When $r<-d/2$, the quadratic variation is given explicitly via an expression that involves the spectral zeta function, which reduces to the Riemann zeta function when $d=1$ and $D$ is an interval.

Evaluation of Reanalysis Estimates of Precipitation, Radiation, and Temperature over Benin (West Africa)

Abstract In West Africa, climatic data issues, especially availability and quality, remain a significant constraint to the development and application of distributed hydrological modeling. As alternatives to ground-based observations, reanalysis products have received increasing attention in recent years. This study aims to evaluate three reanalysis products, namely, ERA5, Water and Global Change (WATCH) Forcing Data (WFD) ERA5 (WFDE5), and MERRA-2, from 1981 to 2019 to determine their ability to represent four hydrological climates variables over a range of space and time scales in Benin. The variables from the reanalysis products are compared with point station databased metrics Kling–Gupta efficiency (KGE), mean absolute error (MAE), correlation, and relative error in precipitation annual (REPA). The results show that ERA5 presents a better correlation for annual mean temperature (between 0.74 and 0.90) than do WFDE5 (0.63–0.78) and MERRA-2 (0.25–0.65). Both ERA5 and WFDE5 are able to reproduce the observed upward trend of temperature (0.2°C decade−1) in the region. We noted a systematic cold bias of ∼1.3°C in all reanalyses except WFDE5 (∼0.1°C). On the monthly time scale, the temperature of the region is better reproduced by ERA5 and WFDE5 (KGE ≥ 0.80) than by MERRA-2 (KGE &lt; 0.5). At all time scales, WFDE5 produces the best MAE scores for longwave (LW) and shortwave (SW) radiation, followed by ERA5. WFDE5 also provides the best estimates for the annual precipitation (REPA ∈ ]−25, 25[ and KGE ≥ 50% at most stations). ERA5 produces similar results, but MERRA-2 performs poorly in all the metrics. In addition, ERA5 and WFDE5 reproduce the bimodal rainfall regime in southern Benin, unlike MERRA-2, but all products have too many small rainfall events.

On Sobolev norms involving Hardy operators in a half-space

We consider Hardy operators on the half-space, that is, ordinary and fractional Schrödinger operators with potentials given by the appropriate power of the distance to the boundary. We show that the scales of homogeneous Sobolev spaces generated by the Hardy operators and by the fractional Laplacian are comparable with each other when the coupling constant is not too large in a quantitative sense. Our results extend those in the whole Euclidean space and rely on recent heat kernel bounds.

Between decoloniality and borders, positionalities and scales in urban space: geographical considerations from the murder of a homeless person in a brazilian city

The murder of a homeless person triggered this geographic consideration. What could be understood as a recurrent scene of urban violence in Brazil conveys a series of spatiotemporal meanings, practices, behaviors and gestures around socio-spatial relations. When thinking about Latin America it is known that, even with the processes of independence and the formal end of colonialism, coloniality and everything associated with it did not disappear. Urban space is a privileged site for thinking about power relations and a varied sort of devices that take shape in public, private and institutional spaces are reproduced in it. In this sense, issues of ethnicity, race, gender, sexuality, territorial origin, cultural background and immigration-related stigmas, need to be understood within a geopolitical, positional and relational framework in a non-hierarchical way, from local to global scale. Given the complexity that comprises the emergence of homeless people, this paper aims to understand their daily life confrontations and the risks of its exclusion or violent eradication. In doing this we ponder about the problem of the positionality of the roles of geographer, researcher, citizen and activist in the search for socio-spatial justice. Such questions are involved in a set of trajectories and positionalities, which appear in the form of annunciations, denunciations and tensions that we hope to present and discuss with the aim of offering alternatives for the geographic practice itself.

An Underwater Dense Small Object Detection Model Based on YOLOv5-CFDSDSE

Underwater target detection is a key technology in the process of exploring and developing the ocean. Because underwater targets are often very dense, mutually occluded, and affected by light, the detection objects are often unclear, and so, underwater target detection technology faces unique challenges. In order to improve the performance of underwater target detection, this paper proposed a new target detection model YOLOv5-FCDSDSE based on YOLOv5s. In this model, the CFnet (efficient fusion of C3 and FasterNet structure) structure was used to optimize the network structure of the YOLOv5, which improved the model’s accuracy while reducing the number of parameters. Then, Dyhead technology was adopted to achieve better scale perception, space perception, and task perception. In addition, the small object detection (SD) layer was added to combine feature information from different scales effectively, retain more detailed information, and improve the detection ability of small objects. Finally, the attention mechanism squeeze and excitation (SE) was introduced to enhance the feature extraction ability of the model. This paper used the self-made underwater small object dataset URPC_UODD for comparison and ablation experiments. The experimental results showed that the accuracy of the model proposed in this paper was better than the original YOLOv5s and other baseline models in the underwater dense small object detection task, and the number of parameters was also reduced compared to YOLOv5s. Therefore, YOLOv5-FCDSDSE was an innovative solution for underwater target detection tasks.

Body, Scale, and Space: Study on the Spatial Construction of Mogao Cave 254

This article focuses on the relationship between body, scale, and space, as revealed in Mogao Cave 254 in Gansu Province. Three topics, namely, body scale, pilgrim behavior, and time–space perception, are discussed. A space model based on mapping and measurement by former scholars is created to facilitate and visualize the analysis of the body scale of the cave space; the restriction of body scale suggests certain pilgrim behavior in the cave, whereas the occurrence of body behavior results in perception in the dimension of time. How time and space are related must be understood to comprehend the motif of Buddhist expression. This study is an architectural approach to spatial analysis that integrates the design, construction, and use phases through the scale, behavior, and perception dimensions. It is dedicated to broadening and enriching the cognitive dimensions of the space value of Mogao caves to reveal the original value of caves as religious spaces and completely preserve their material and invisible cultural heritage.

Alternative Scale(ing) Practices in Architectural Design Studio

Commonly associated with the accurate representation of reality according to a specific ratio, which depends mainly on measuring and precision, the notion of scale has been considered an operative tool in architecture to perceive and indicate the size of anything according to a reference system. An effort to challenge the concept of scale(ing) to include diverse reference units and their scalar articulations holds the potential of instigating novel associations in architectural design studios. Possible readings of these alternative scale(ing) conditions are discussed in the paper in reference to a series of design exercises conducted at architectural design studios. The main intention is to question these unmediated associations to the reference unit in the determination of size and proportion, thus possible scales of architectural space. This particular method of analysis aims to arrive at various scale(ing) conditions, whose internal reference system is challenged by the changes and shifts in the definition of the major reference unit. Each different scale(ing) condition studied through the given exercise was regarded as an act of confrontation for readings of scale as well as presenting provocative challenges to the architectural design processes. With this standpoint, the paper proposes a multi-layered reading of scale that argues the reference unit’s instrumentality in the definition of architectural space and proposes an alternative methodology of integrating scale in architectural design education.

Accelerating Thermal Simulations in Additive Manufacturing by Training Physics-Informed Neural Networks With Randomly Synthesized Data

Abstract The temperature history of an additively manufactured part plays a critical role in determining process–structure–property relationships in fusion-based additive manufacturing (AM) processes. Therefore, fast thermal simulation methods are needed for a variety of AM tasks, from temperature history prediction for part design and process planning to in situ temperature monitoring and control during manufacturing. However, conventional numerical simulation methods fall short in satisfying the strict requirements of time efficiency in these applications due to the large space and time scales of the required multiscale simulation. While data-driven surrogate models are of interest for their rapid computation capabilities, the performance of these models relies on the size and quality of the training data, which is often prohibitively expensive to create. Physics-informed neural networks (PINNs) mitigate the need for large datasets by imposing physical principles during the training process. This work investigates the use of a PINN to predict the time-varying temperature distribution in a part during manufacturing with laser powder bed fusion (L-PBF). Notably, the use of the PINN in this study enables the model to be trained solely on randomly synthesized data. These training data are both inexpensive to obtain, and the presence of stochasticity in the dataset improves the generalizability of the trained model. Results show that the PINN model achieves higher accuracy than a comparable artificial neural network trained on labeled data. Further, the PINN model trained in this work maintains high accuracy in predicting temperature for laser path scanning strategies unseen in the training data.

An integrated satellite characterization and hydrodynamic study in assessing coastal dynamics in Cirebon, West Java

Located on the northern coast of Java Island, the coastal area of Cirebon is an area with rapid population growth and extensively developed as settlement areas, industrial estates, fisheries, agriculture, and public service infrastructures. Coastal dynamics issues have been occurring in the coastal area of Cirebon, which is characterized by significant changes in shoreline position. Moreover, some critical infrastructures are not functioning properly and may cause high maintenance costs and economic loss to the related stakeholders. This study aims to provide a greater understanding of the coastal dynamics along the 73.16 km of Cirebon shoreline through remote sensing and GIS methods supported by hydrodynamic analysis. This study applied coastal characterization, coastal structure identifications, and coastal processes assessments, including erosion and accretion analysis, hydrodynamic and tidal analysis, and inundation projection. The study indicates that some areas have been subject to significant erosion and accretion during the last two decades, with the total lengths of the eroded and accreted shoreline being −21.9 km (35.6%) and ＋39.6 km (64.4%), respectively. Furthermore, the coastal structures significantly influence the coastline’s dynamic and mitigate the negative impacts of coastal floods. Jetty access channels in the west area have caused partial coastal erosion, while the breakwaters in the east area accelerated sedimentation processes and improved the growth of mangroves simultaneously. This study also simulated the potentially inundated areas by 2050 in four scenarios based on projected sea level rise, land subsidence, and extreme wave events. Policies to prevent such potential hazards are needed to avoid negative impacts on the living quality and vital economic sectors. This study provides insight into coastal characterization, potential coastal hazards, and their driving processes in time and space scale for coastal adaptation planning policies and sustainable coastal hazard management and mitigation.

Building Information Modeling and Building Performance Simulation-Based Decision Support Systems for Improved Built Heritage Operation

Adapting outdated building stocks’ operations to meet current environmental and economic demands poses significant challenges that, to be faced, require a shift toward digitalization in the architecture, engineering, construction, and operation sectors. Digital tools capable of acquiring, structuring, sharing, processing, and visualizing built assets’ data in the form of knowledge need to be conceptualized and developed to inform asset managers in decision-making and strategic planning. This paper explores how building information modeling and building performance simulation technologies can be integrated into digital decision support systems (DSS) to make building data accessible and usable by non-digital expert operators through user-friendly services. The method followed to develop the digital DSS is illustrated and then demonstrated with a simulation-based application conducted on the heritage case study of the Faculty of Engineering in Bologna, Italy. The analysis allows insights into the building’s energy performance at the space and hour scale and explores its relationship with the planned occupancy through a data visualization approach. In addition, the conceptualization of the DSS within a digital twin vision lays the foundations for future extensions to other technologies and data, including, for example, live sensor measurements, occupant feedback, and forecasting algorithms.

Research on a Knowledge Graph Embedding Method Based on Improved Convolutional Neural Networks for Hydraulic Engineering

In response to the shortcomings of existing knowledge graph embedding strategies, such as weak feature interaction and latent knowledge representation, a unique hydraulic knowledge graph embedding method is suggested. The proposed method incorporates spatial position features into the entity-relation embedding process, thereby enhancing the representation capability of latent knowledge. Furthermore, it utilizes a multi-layer convolutional neural network to fuse features at different levels, effectively capturing more abundant semantic information. Additionally, the method employs multi-scale dilated convolution kernels to capture rich explicit interaction features across different scales of space. In this study, the effectiveness of the proposed model was validated on the link prediction task. Experimental results demonstrated that, compared to the ConvE model, the proposed model achieved a significant improvement of 14.8% in terms of mean reciprocal rank (MRR) on public datasets. Additionally, the suggested model outperformed the ConvR model on the hydraulic dataset, leading to a 10.1% increase in MRR. The results indicate that the proposed approach exhibits good applicability and performance in the task of hydraulic knowledge graph complementation. This suggests that the method has the potential to offer significant assistance for knowledge discovery and application research in the field of hydraulics.

Promoting ecological restoration of deeply urbanized hilly areas: A multi-scale ecological networks approach

With the deep expansion of urbanization, contradictions between human beings and the natural environment in hilly areas have shown the characteristics of multi-scale and complexity. The traditional construction of ecological network rarely focused on the relationship between ecological elements at different scales, which made it difficult to systematically solve the ecological problems. To address the gap, this paper proposed a novel framework to construct multi-scale ecological networks based on the discussion of the ecological environment problems at different space scales. Firstly, one of the most deeply urbanized hilly area in China (Changsha- Zhuzhou- Xiangtan urban agglomeration, CZXUA) was chosen as the study area. By constructing the multi-scale evaluation system of ecological patches importance (EPI) after conducting the Morphological Spatial Pattern Analysis (MSPA), the identification method of ecological sources was optimized at different scales. Then, spatial principal component analysis (SPCA) was used to sort out the resistance factors at different scales, and the multi-scale resistance surfaces was constructed accordingly. Furthermore, the multi-scale ecological networks from the CZXUA to the central urban area were cooperatively constructed through the Least-cost path (LCP), circuit theory, hierarchical transmission and scale nesting. Finally, based on characteristics of the ecological networks at different scales, the study constructed the overall ecological security pattern in urban agglomeration, the ecological space development framework in metropolitan area and the green open space system in central urban area, which was well applied to the territorial space planning at different spacial scales. The results made up for the deficiency of single-scale ecological network in neglecting the important details of local areas, and provided a feasible ecological restoration plan for the decision makers and practitioners. This study can contribute to cross-scale ecosystem restoration and biodiversity conservation in hills.

Emergence of Hydrodynamic Spatial Long-Range Correlations in Nonequilibrium Many-Body Systems.

At large scales of space and time, the nonequilibrium dynamics of local observables in extensive many-body systems is well described by hydrodynamics. At the Euler scale, one assumes that each mesoscopic region independently reaches a state of maximal entropy under the constraints given by the available conservation laws. Away from phase transitions, maximal entropy states show exponential correlation decay, and independence of fluid cells might be assumed to subsist during the course of time evolution. We show that this picture is incorrect: under ballistic scaling, regions separated by macroscopic distances "develop long-range correlations as time passes." These correlations take a universal form that only depends on the Euler hydrodynamics of the model. They are rooted in the large-scale motion of interacting fluid modes and are the dominant long-range correlations developing in time from long-wavelength, entropy-maximized states. They require "the presence of interaction" and "at least two different fluid modes" and are of a fundamentally different nature from well-known long-range correlations occurring from diffusive spreading or from quasiparticle excitations produced in far-from-equilibrium quenches. We provide a universal theoretical framework to exactly evaluate them, an adaptation of the macroscopic fluctuation theory to the Euler scale. We verify our exact predictions in the hard-rod gas, by comparing with numerical simulations and finding excellent agreement.

Optical and SAR Image Registration Based on Pseudo-SAR Image Generation Strategy

The registration of optical and SAR images has always been a challenging task due to the different imaging mechanisms of the corresponding sensors. To mitigate this difference, this paper proposes a registration algorithm based on a pseudo-SAR image generation strategy and an improved deep learning-based network. The method consists of two stages: a pseudo-SAR image generation strategy and an image registration network. In the pseudo-SAR image generation section, an improved Restormer network is used to convert optical images into pseudo-SAR images. An L2 loss function is adopted in the network, and the loss function fluctuates less at the optimal point, making it easier for the model to reach the fitting state. In the registration part, the ROEWA operator is used to construct the Harris scale space for pseudo-SAR and real SAR images, respectively, and each extreme point in the scale space is extracted and added to the keypoint set. The image patches around the keypoints are selected and fed into the network to obtain the feature descriptor. The pseudo-SAR and real SAR images are matched according to the descriptors, and outliers are removed by the RANSAC algorithm to obtain the final registration result. The proposed method is tested on a public dataset. The experimental analysis shows that the average value of NCM surpasses similar methods over 30%, and the average value of RMSE is lower than similar methods by more than 0.04. The results demonstrate that the proposed strategy is more robust than other state-of-the-art methods.

Dendritic propagation on circular electrodes: The impact of curvature on the packing density.

The dendritic growth in rechargeable batteries is one of the hurdles for the utilization of high energy-density elements, such as alkaline metals, as the electrode. Herein we explore the preventive role of the curved electrode surface in the cylindrical electrode design versus the flat geometry on the stochastic evolution of the dendritic crystals. In this regard we establish a coarse-grained Monte Carlo paradigm in the polar coordinates (r,θ), which runs in a larger scale of time and space (∼μs,∼nm ) than those of interionic collisions (∼fs, Å). Subsequently we track the density and the maximum reach of the microstructures in real time, and we elaborate on the underlying mechanisms for their correlation of the relative dendrite measure with the electrode curvature. Such quantification of the positive impact of the curvature on suppressing dendrites could be utilized as an effective longevity design parameter, particularly for the cases prone to dendritic propagation.