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The primary objective of this study is to tackle the enduring challenge of preserving feature integrity during the manipulation of geometric data in computer graphics. Our work aims to introduce and validate a variational sparse diffusion model that enhances the capability to maintain the definition of sharp features within meshes throughout complex processing tasks such as segmentation and repair.

We developed a variational sparse diffusion model that integrates a high-order L₁ regularization framework with Dirichlet boundary constraints, specifically designed to preserve edge definition. This model employs an innovative vertex updating strategy that optimizes the quality of mesh repairs. We leverage the augmented Lagrangian method to address the computational challenges inherent in this approach, enabling effective management of the trade-off between diffusion strength and feature preservation. Our methodology involves a detailed analysis of segmentation and repair processes, focusing on maintaining the acuity of features on triangulated surfaces.

Our findings indicate that the proposed variational sparse diffusion model significantly outperforms traditional smooth diffusion methods in preserving sharp features during mesh processing. The model ensures the delineation of clear boundaries in mesh segmentation and achieves high-fidelity restoration of deteriorated meshes in repair tasks. The innovative vertex updating strategy within the model contributes to enhanced mesh quality post-repair. Empirical evaluations demonstrate that our approach maintains the integrity of original, sharp features more effectively, especially in complex geometries with intricate detail.

The originality of this research lies in the novel application of a high-order L₁ regularization framework to the field of mesh processing, a method not conventionally applied in this context. The value of our work is in providing a robust solution to the problem of feature degradation during the mesh manipulation process. Our model’s unique vertex updating strategy and the use of the augmented Lagrangian method for optimization are distinctive contributions that enhance the state-of-the-art in geometry processing. The empirical success of our model in preserving features during mesh segmentation and repair presents an advancement in computer graphics, offering practical benefits to both academic research and industry applications.

This paper aims to focus on solving the path optimization problem by modifying the probabilistic roadmap (PRM) technique as it suffers from the selection of the optimal number of nodes and deploy in free space for reliable trajectory planning.

Traditional PRM is modified by developing a decision-making strategy for the selection of optimal nodes w.r.t. the complexity of the environment and deploying the optimal number of nodes outside the closed segment. Subsequently, the generated trajectory is made smoother by implementing the modified Bezier curve technique, which selects an optimal number of control points near the sharp turns for the reliable convergence of the trajectory that reduces the sum of the robot’s turning angles.

The proposed technique is compared with state-of-the-art techniques that show the reduction of computational load by 12.46%, the number of sharp turns by 100%, the number of collisions by 100% and increase the velocity parameter by 19.91%.

The proposed adaptive technique provides a better solution for autonomous navigation of unmanned ground vehicles, transportation, warehouse applications, etc.

This case is about “Red”, a cross-border e-commerce platform developed from a community which was built to share overseas shopping experience. With sharp insights into the consumption behavior of urban white-collar women and riding on its community e-commerce advantage, “Red”, a cross-border e-commerce startup, pulled in three rounds of financing within just 16 months regardless of increasingly competitive market. On the other hand, well-established platforms such as T-mall International and Joybuy also stepped in, and their involvement will also speed up the industry integration and usher in a reshuffling period. Confronted with the “price war” started by those e-commerce giants, in what ways can “Red” adjust its shopping experience and after-sales services to enhance the brand value and sharpen its edge?

The purpose of this study is to investigate the influence of enclosure shape on magnetohydrodynamic (MHD) nanofluidic flow, heat transfer and irreversibility in square, trapezoidal and triangular thermal systems under fluid volume constraints, with the aim of optimizing thermal behavior in diverse applications.

The study uses numerical simulations based on a finite element-based technique to analyze the effects of the Rayleigh number (Ra), Hartmann number (Ha), magnetic field orientation (γ) and nanoparticle concentration (ζ) on heat transfer characteristics and thermodynamic entropy production.

The key findings reveal that the geometrical design significantly influences fluid velocity, heat transfer and irreversibility. Trapezoidal thermal systems outperform square systems, while triangular systems achieve optimal enhancement. Nanoparticle concentration enhances heat transfer and flow strength at higher Rayleigh numbers. The magnetic field intensity has a significant impact on fluid flow and heat transport in natural convection, with higher Hartmann numbers resulting in reduced flow strength and heat transfer. The study also highlights the influence of various parameters on thermodynamic entropy production.

Further research can explore additional geometries, parameters and boundary conditions to expand the understanding of enclosure shape effects on MHD nanofluidic flow and heat transfer. Experimental validation can complement the numerical simulations presented in this study.

This study provides valuable insights into the impact of enclosure shape on heat transfer performance in MHD nanofluid flow systems. The findings contribute to the optimization of thermal behavior in applications such as electronics cooling and energy systems. The comparison of different enclosure shapes and the analysis of thermodynamic entropy production add novelty to the study.

The purpose of this study is to introduce a new type of sensor which uses microwave metamaterials and direct-coupled split-ring resonators (DC-SRRs) to measure the dielectric properties of solid materials in real time. The sensor uses a transmission line with a bridge-type structure to measure the differential frequency, which can be used to calculate the dielectric constant of the material being tested. The study aims to establish an empirical relationship between the dielectric properties of the material and the frequency measurements obtained from the sensor.

In the proposed design, the opposite arm of the bridge transmission line is loaded by DC-SRRs, and the distance between DC-SRRs is optimized to minimize the mutual coupling between them. The DC-SRRs are loaded with the material under test (MUT) to perform differential permittivity sensing. When identical MUT is placed on both resonators, a single transmission zero (notch) is obtained, but non-identical MUTs exhibit two split notches. For the design of differential sensors and comparators based on symmetry disruption, frequency splitting is highly useful.

The proposed structure is demonstrated using electromagnetic simulation, and a prototype of the proposed sensor is fabricated and experimentally validated to prove the differential sensing principle. Here, the sensor is analyzed for sensitivity by using different MUTs with relative permittivity ranges from 1.006 to 10 and with a fixed dimension of 9 mm × 10 mm ×1.2 mm. It shows a very good average frequency deviation per unit change in permittivity of the MUTs, which is around 743 MHz, and it also exhibits a very high average relative sensitivity and quality factor of around 11.5% and 323, respectively.

The proposed sensor can be used for differential characterization of permittivity and also as a comparator to test the purity of solid dielectric samples. This sensor most importantly strengthens robustness to environmental conditions that cause cross-sensitivity or miscalibration. The accuracy of the measurement is enhanced as compared to conventional single- and double-notch metamaterial-based sensors.

Regional comprehensive economic partnership (RCEP) is understood as the world's largest trading bloc given its contribution to the world output (30%). The mega trade bloc brings together 15 countries of East Asia, Southeast Asia and Oceania to eliminate tariff and non-tariff barriers in goods and services trade. The study suggests the importance of sector specific reforms for Malaysia to strengthen domestic capability.

The analytical framework constructs upon the partial equilibrium analysis and uses WITS SMART simulations.

The study finds that Malaysia's elimination of tariffs under the RCEP will cause a surge in imports from developed member countries of RCEP like Australia, South Korea and Japan. The study also finds a trade diversion in countries such as India. The empirical results establishes that RCEP would further strengthen intra-ASEAN trade.

The study explores select sectors of the manufacturing industry in Malaysia.

The implementation of RCEP would impact the manufacturing sector immensely, especially in sectors like electrical machinery and equipment and inorganic chemicals, which are two of the major trading commodities of the Malaysian economy.

Any trade agreement has a larger impact on the society. It may raise income, boost the consumer preferences and create or erode consumer welfare. The study reports the consumer welfare effect of the implementation of RCEP in Malaysia.

The study is the first attempt to do a partial equilibrium analysis for the electrical machinery and equipment sector and inorganic chemicals sector of Malaysia using both aggregated and disaggregated data at HS two-digit and HS six-digit level.

The purpose of this paper is twofold. Firstly, the paper aims to determine the separate effects of the COVID-19 pandemic and government actions represented by the index of stringency, containment and economic support on the attractiveness of foreign direct investment (FDI). Secondly, the paper aims to explore the impact of the interactions between the COVID-19 epidemic and government interventions on FDI.

The study uses a panel data set of 30 Asian countries during the two pandemic years 2020 and 2021 to investigate the effect of government actions on the resilience of FDI attractiveness factors.

The empirical results reveal the negative effect of COVID-19 on FDI inflows and attractiveness factors. However, government responses have a positive and statistically significant effect on the FDI attractiveness factors such as economic growth, trade openness and human and technological capital development and contribute to the economic recovery of the Asian region.

The empirical findings can provide useful information for policymakers in designing macroeconomic policies and taking government measures to improve their investment environment and attract FDI.

The study shows that government responses, economic support, containment and health policies are effective in containing viruses, reducing the impact of the COVID-19 pandemic and strengthening resilience in FDI attractiveness factors. It also indicates that foreign investors are responding positively to government measures.