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An optimized robot path-planning algorithm is required for various aspects of robot movements in applications. The efficacy of the robot path-planning model is vulnerable to the number of search nodes, path cost, and time complexity. The conventional A-star (A*) algorithm outperforms other grid-based algorithms because of its heuristic approach. However, the performance of the conventional A* algorithm is suboptimal for the time, space, and number of search nodes, depending on the robot motion block (RMB). To address these challenges, this paper proposes an optimal RMB with an adaptive cost function to improve performance. The proposed adaptive cost function keeps track of the goal node and adaptively calculates the movement costs for quickly arriving at the goal node. Incorporating the adaptive cost function with a selected optimal RMB significantly reduces the searches of less impactful and redundant nodes, which improves the performance of the A* algorithm in terms of the number of search nodes and time complexity. To validate the performance and robustness of the proposed model, an extensive experiment was conducted. In the experiment, an open-source dataset featuring various types of grid maps was customized to incorporate the multiple map sizes and sets of source-to-destination nodes. According to the experiments, the proposed method demonstrated a remarkable improvement of 93.98% in the number of search nodes and 98.94% in time complexity compared to the conventional A* algorithm. The proposed model outperforms other state-of-the-art algorithms by keeping the path cost largely comparable. Additionally, an ROS experiment using a robot and lidar sensor data shows the improvement of the proposed method in a simulated laboratory environment.

Using the Time-Varying Parameter Stochastic Volatility Vector Autoregression (TVP-SV-VAR) model within a Bayesian inference framework, this study examines the spillover effects of BRICS economies’ monetary policies on the Renminbi (RMB) exchange rates and China’s trade balance from November 2006 to December 2022. The spillover effects are categorized into interest rate effects and monetary effects. The empirical findings reveal that, through the interest rate effect channel, the monetary policies of BRICS countries exert a detrimental spillover effect on the RMB exchange rate, while initially benefiting but ultimately harming China’s trade balance in the short and long term, respectively. Conversely, under the monetary effect channel, these policies adversely impact the RMB exchange rate but beneficially influence China’s trade balance. Consequently, it is imperative to develop a robust early warning system for economic policy indicators in emerging economies such as BRICS, enhance policy cooperation among BRICS nations, and foster coordinated advancement of international trade and cross-border capital flows within the BRICS consortium.

Computerized brain tumor classification from the reconstructed microwave brain (RMB) images is important for the examination and observation of the development of brain disease. In this paper, an eight-layered lightweight classifier model called microwave brain image network (MBINet) using a self-organized operational neural network (Self-ONN) is proposed to classify the reconstructed microwave brain (RMB) images into six classes. Initially, an experimental antenna sensor-based microwave brain imaging (SMBI) system was implemented, and RMB images were collected to create an image dataset. It consists of a total of 1320 images: 300 images for the non-tumor, 215 images for each single malignant and benign tumor, 200 images for each double benign tumor and double malignant tumor, and 190 images for the single benign and single malignant tumor classes. Then, image resizing and normalization techniques were used for image preprocessing. Thereafter, augmentation techniques were applied to the dataset to make 13,200 training images per fold for 5-fold cross-validation. The MBINet model was trained and achieved accuracy, precision, recall, F1-score, and specificity of 96.97%, 96.93%, 96.85%, 96.83%, and 97.95%, respectively, for six-class classification using original RMB images. The MBINet model was compared with four Self-ONNs, two vanilla CNNs, ResNet50, ResNet101, and DenseNet201 pre-trained models, and showed better classification outcomes (almost 98%). Therefore, the MBINet model can be used for reliably classifying the tumor(s) using RMB images in the SMBI system.

Purpose ― This paper aims to empirically investigate the impact of currency swaps on international trade, given China's differential level of financial development and its currency swap partners. Methods ― The study employes an empirical structural gravity model using datasets encompassing financial development, trade, and intuitive gravity equation variables for 27 countries from 1980 to 2013. The level of financial development and swaps was captured by the interaction term of the disaggregated measure of financial development, such as access, depth, and efficiency, each interacting with currency swaps.Findings ― The findings suggest that currency swaps are essential for trade and exhibit a large trade effect, especially for countries with relatively low levels of financial development. The paper substantiates empirical evidence indicating disparities in financial development across countries, and such differences are important in determining trade patterns. Implication ― Strong financial systems promote trade in advanced economies, whereas the opposite holds true for developing countries. The examination of the influence of financial systems on trade through empirical tests remains important on the research agenda of policymakers and researchers, especially those looking at industry-level import and export data.Originality ― The study delves into the nexus between financial development and trade within the framework of the Central Bank bilateral currency swap network by highlighting the role of financial institutions and market size (depth), activity (access), and efficiency. In addition, it addresses the drawbacks of previous empirical research that largely focuses on the private credit-to-GDP ratio as a key proxy for financial development.

Since 2009, the Chinese government has committed itself to encourage Renminbi (RMB) internationalization. China has introduced various direct steps supporting the RMB internationalization, including the open capital market in Hugangtong, the growth of the offshore RMB, the launch of the Cross-Border Interbank Payment System (CIPS). The reform of the exchange rate structure for the RMB, developing crude oil futures. Incorporating the RMB into the SDR, BRI, and the establishment of AIIB contribute to further internationalization of the RMB. The BRI provides an opportunity, on the one hand, that the RMB becomes a foreign trade currency and strengthens the RMB's position as an international currency. China must encourage RMB internationalization, including turning cross-border RMB trade as a payment currency, promoting RMB internationalization with ODI, enhancing the RMB exchange rate process, developing domestic financial system and financial infrastructure, and warning to expand capital account and develop offshore RMB.

Drawing on untapped, open-source commentaries from elite Chinese commentators, we show that since 2018, China has become fixated on preparations for a potential “financial war” with the United States. China appears to have adjusted its renminbi (RMB) internationalization strategy – and broader approach to financial regulation – accordingly. China's perceived vulnerability to dollar sanctions increases the urgency of RMB internationalization but also makes it unacceptably risky to liberalize the capital account. The sources suggest that China's solution is to pursue partial RMB internationalization by keeping the capital account tightly regulated while encouraging trade partners to sign up for RMB settlement platforms. As more foreign banks sign up for RMB payment systems, China is gaining confidence that its financial system could weather the sudden imposition of dollar sanctions. However, China remains vulnerable in principle to a “monetary sniping” attack on the offshore RMB market.

Traditional methods for assessing the stability of rubble mound breakwaters (RMBs) often rely on 2.5D data, which may fall short in capturing intricate changes in the armor units, such as tilting and lateral shifts. Achieving a detailed analysis of RMB geometry typically requires fully 3D methods, but these often hinge on expensive acquisition technologies like terrestrial laser scanning (TLS) or airborne light detection and ranging (LiDAR). This article introduces an innovative approach to evaluate the structural stability of RMBs by integrating UAV-based photogrammetry and the random sample consensus (RANSAC) algorithm. The RANSAC algorithm proves to be an efficient and scalable tool for extracting primitives from point clouds (PCs), effectively addressing challenges presented by outliers and data noise in photogrammetric PCs. Photogrammetric PCs of the RMB, generated using Structure-from-Motion and MultiView Stereo (SfM-MVS) from both pre- and post-storm flights, were subjected to the RANSAC algorithm for plane extraction and segmentation. Subsequently, a spatial proximity criterion was employed to match cuboids between the two time periods. The methodology was validated on the detached breakwater of Cabedelo do Douro in Porto, Portugal, with a specific focus on potential rotations or tilting of Antifer cubes within the protective layer. The results, assessing the effects of the Leslie storm in 2018, demonstrate the potential of our approach in identifying and quantifying structural changes in RMBs.

A sustainable strategy was developed to valorize rabbit manure waste by synthesizing a porous quaternary Ni-Co-Zn-Fe layered double hydroxide/biochar nanocomposite (QL-RMB) for the efficient removal of Mn(VII) in the form of permanganate (MnO4−) from aqueous solutions. The QL-RMB adsorbent exhibited a well-developed mesoporous structure with uniformly dispersed nanoparticles, achieving 73% MnO4− removal within 60 min under optimized conditions (pH 3.0; dosage 0.5 g L−1). Adsorption followed pseudo-second-order kinetics and was best described by the Freundlich isotherm model (R2 > 0.98), yielding a maximum Langmuir adsorption capacity (qmax) of 45.13 mg g−1. Statistical physics modeling confirmed a multi-ionic, vertically oriented adsorption configuration, while thermodynamic analysis demonstrated that the process was spontaneous and exothermic, governed by electrostatic attraction, anion exchange, and surface complexation. The QL-RMB composite exhibited excellent MnO4− selectivity in the presence of competing ions (selectivity coefficients: 24.96 for Fe3+, 31.59 for Ni2+, 23.56 for Zn2+) and retained significant removal efficiency (73.96%) after five regeneration cycles. In a circular economy approach, the Mn (VII)-spent adsorbent (QL-RMB/Mn) was valorized as an electrocatalyst for urea electro-oxidation, achieving a current density of ~127.19 mA cm−2 for pristine QL-RMB, which increased to ~217.07 mA cm−2 after Mn(VII) adsorption (QL-RMB/Mn) in 1 M KOH/1 M urea. Batch scale-up studies revealed an efficiency of 42.55 g or 95% MnO4− removal from 50 L water, with a low estimated production cost of 0.0602 USD g−1. Environmental sustainability was confirmed by the National Environmental Methods Index (NEMI), modified Green Analytical Procedure Index (Mo-GAPI), Eco-scale (score: 77), and Analytical GREEness (AGREE) assessment frameworks.

Digital transformations are impacting inter-state currency politics in strategically important ways. This article contributes to growing debates over the nature of US-China competition as the Chinese RMB rapidly digitizes in ways said to challenge the hegemony of a slower digitizing greenback. We categorize existing views of currency competition in the digital age into two categories: ‘conventional transformation’ and ‘transformative continuity’. Both these presently dominant perspectives, we argue, are overly techno-deterministic and stand in contrast to a third perspective we propose called ‘probabilistic flux’. Emphasizing the unanticipated and error-prone nature of technological change through Social Construction of Technology theory and informed by the IPE of monetary relations, we provide a more nuanced assessment of digital RMB’s challenges to dollar dominance stressing the functions, benefits and powers of international currency hegemony. Our conclusions are three-fold. First, wider digital currency alternatives to both the dollar and RMB have enriched the international currency functions of the former over the latter. Second, this broader array of digital currency alternatives combines with Chinese RMB digitization to gradually erode the functional base and benefits of dollar dominance position, as well as diminish the US’s international monetary power in both Asia and beyond. Third, what we see as largely friendly digital currency competition focused on domestic imperatives currently remains unpredictable. These findings pose present possibilities for greater international cooperation but equally for less friendly competition and flux particularly as US dollar digitization also unfolds in ways that are difficult to anticipate.