Search Results (104)

This study applies a deep neural network (DNN) to optimize the 22.5 m pontoon hull form of a small waterplane area twin hull (SWATH) vessel with fin stabilizers, aiming to reduce calm water resistance at a Froude number of 0.8 under even keel conditions. The vessel’s resistance is simplified into three components: pontoon, strut, and fin stabilizer. Four design parameters define the pontoon geometry: fore-body length, aft-body length, fore-body angle, and aft-body angle. Computational fluid dynamics (CFD) simulations using STAR-CCM+ 2302 provide 1400 resistance data points, including fin stabilizer lift and drag forces at varying angles of attack. These are used to train a DNN in MATLAB 2018a with five hidden layers containing six, eight, nine, eight, and seven neurons. K-fold cross-validation ensures model stability and aids in identifying optimal design parameters. The optimized hull has a 7.8 m fore-body, 6.8 m aft-body, 10$°$ fore-body angle, and 35$°$ aft-body angle. It achieves a 2.2% resistance reduction compared to the baseline. The improvement is mainly due to a reduced Munk moment, which lowers the angle of attack needed by the fin stabilizer, thereby reducing drag. The optimized design provides cost-efficient construction and enhanced payload capacity. This study demonstrates the effectiveness of combining CFD and deep learning for hull form optimization. Full article

Fin-stabilized guided rockets exhibit ballistic characteristics such as low initial velocity, high flight altitude, and long flight duration, which render their impact point accuracy and flight stability highly susceptible to the influence of wind. In this paper, the four-dimensional nonlinear angular motion equations describing the changes in attack angle and the law of axis swing of a dual-spin rocket are established, and the phase trajectory and equilibrium point stability characteristics of the nonlinear angular motion system under windy conditions are analyzed. Aiming at the problem that the equilibrium point of the angular motion system cannot be solved analytically with the change in wind speed, a phase trajectory projection sequence method based on the Poincaré cross-section and stroboscopic mapping is proposed to analyze the effect of wind on the angular motion bifurcation characteristics of a dual-spin rocket. The possible instability of angular motion caused by nonlinear aerodynamics under strong wind conditions is explored. This study is of reference significance for the launch control and aerodynamic design of guided rockets in complex environments. Full article

This paper presents a bionic dual-fin underwater robot, inspired by the ocean sunfish, that achieves multiple swimming motions using only two vertically arranged fins. This work demonstrates that a mechanically simple platform can execute complex 2-D and 3-D motions through advanced control strategies, eliminating the need for auxiliary actuators. We control the two fins independently so that they can perform cooperative actions in the water, enabling the robot to perform various motions, including high-speed cruising, agile turning, controlled descents, proactive ascents, and continuous spiraling. The swimming performance of the dual-fin robot in executing multi-modal locomotion is experimentally analyzed through visual measurement methods and onboard sensors. Experimental results demonstrate that a minimalist dual-fin propulsion system of the designed ocean sunfish robot can provide speed (maximum cruising speed of 1.16 BL/s), stability (yaw amplitude less than 4.2°), and full three-dimensional maneuverability (minimum turning radius of 0.89 BL). This design, characterized by its simple structure, multiple motion capabilities, and excellent motion performance, offers a promising pathway for developing robust and versatile robots for diverse underwater applications. Full article

This paper explores the effectiveness of an innovative FinTech risk-scoring model to predict the risk-appropriate return for short-term credit sales. The risk score serves to mitigate the information asymmetry between the seller of receivables (“Seller”) and the purchaser (“Funder”), at the same time providing an opportunity for the Funder to earn returns as well as to diversify its portfolio on a risk-appropriate basis. Selling receivables/credit to potential Funders at a risk-appropriate discount also helps Sellers to maintain their short-term financial liquidity and provide the necessary cash flow for operations and other immediate financial needs. We use 18,304 short-term credit-sale transactions between 23 April 2020 and 30 September 2022 from the private FinTech startup Crowdz and its Sustainability, Underwriting, Risk & Financial (SURF) risk-scoring system to analyze the risk/return relationship. The data includes risk scores for both Sellers of receivables (e.g., invoices) along with the Obligors (firms purchasing goods and services from the Seller) on those receivables and provides, as outputs, the mutual gains by the Sellers and the financial institutions or other investors funding the receivables (i.e., the Funders). Our analysis shows that the SURF Score is instrumental in mitigating the information asymmetry between the Sellers and the Funders and provides risk-appropriate periodic returns to the Funders across industries. A comparative analysis shows that the use of SURF technology generates higher risk-appropriate annualized internal rates of return (IRR) as compared to nonuse of the SURF Score risk-scoring system in these transactions. While Sellers and Funders enter into a win-win relationship (in the absence of a default), Sellers of credit instruments are not often scored based on the potential diversification by industry classification. Crowdz’s SURF technology does so and provides Funders with diversification opportunities through numerous invoices of differing amounts and SURF Scores in a wide range of industries. The analysis also shows that Sellers generally have lower financing stability as compared to the Obligors (payers on receivables), a fact captured in the SURF Scores. Full article

This study aims to evaluate how key financial, educational, technological, and institutional drivers shape resource efficiency (RCE), a critical pillar of sustainable development—across major economies. Enhancing RCE is vital for ensuring long-term ecological and economic stability while meeting global sustainability targets. Using panel data from 2000 to 2022 for G20 countries, this research examines the dynamic effects of natural resources (NRSs), educational quality (EDQ), financial inclusion (FIN), green innovation (GRI), and economic complexity (ECC) on RCE. The Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) model is applied to capture both short- and long-term relationships and is validated by robustness checks using the Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) estimators. The results show that EDQ and FIN exert a negative influence on RCE, suggesting that governance inefficiencies occur when aligning education systems and financial mechanisms with sustainability goals. In contrast, NRS, GRI, and ECC significantly enhance RCE, underscoring the value of resource stewardship, innovation-driven transitions, and complex economic structures in promoting efficiency. These findings have governance implications, emphasizing the need for institutional reforms that integrate sustainability into the education and financial sectors while supporting green innovation and economic diversification. Policymakers in G20 economies are urged to implement coherent strategies that redirect educational and financial frameworks toward inclusive, resilient, and resource-efficient development pathways, thereby advancing the United Nations Sustainable Development Goals (SDGs). Full article

The integration of artificial intelligence (AI) and advanced deep learning techniques is reshaping intelligent financial forecasting and decision-support systems. This study presents a comprehensive comparative analysis of advanced deep learning models, including state-of-the-art transformer architectures and established non-transformer approaches, for long-term stock market index prediction. Utilizing historical data from major global indices (S&P 500, NASDAQ, and Hang Seng), we evaluate ten models across multiple forecasting horizons. A dual-metric evaluation framework is employed, combining traditional predictive accuracy metrics with critical financial performance indicators such as returns, volatility, maximum drawdown, and the Sharpe ratio. Statistical validation through the Mann–Whitney U test ensures robust differentiation in model performance. The results highlight that model effectiveness varies significantly with forecasting horizons and market conditions—where transformer-based models like PatchTST excel in short-term forecasts, while simpler architectures demonstrate greater stability over extended periods. This research offers actionable insights for the development of AI-driven intelligent financial forecasting systems, enhancing risk-aware investment strategies and supporting practical applications in FinTech and smart financial analytics. Full article

With the increasing adoption of technologies such as mobile banking and blockchain, the banking sector in developing and emerging economies is experiencing both opportunities and challenges. This study examines the impact of FinTech on bank risk-taking and profitability in the small island economy of Fiji, spanning the period from 2000 to 2024. We employ a fixed-effects model and conduct robustness checks using random effects, pooled ordinary least squares (OLS), and the generalized method of moments (GMM) method, focusing on seven banks (five commercial banks and two non-bank financial institutions). Our analysis evaluates the effect of FinTech while controlling for other bank-specific factors that may influence risk-taking and profitability. The results indicate that FinTech development significantly reduces bank risk-taking and enhances profitability, suggesting a positive and substantial impact on financial performance and stability. The findings highlight the need for banks operating in Fiji and similar small economies to continue and expand their investments in FinTech innovations. Furthermore, the study suggests that regulatory bodies and policymakers should strengthen institutional and regulatory frameworks to support and guide FinTech’s evolution within the banking sector. Full article

The integration of biomimetic pectoral fins into robotic fish presents a promising approach to enhancing maneuverability, stability, and braking efficiency in underwater robotics. This study investigates a 1-DOF (degree of freedom) pectoral fin mechanism integrated into the SpineWave robotic fish. Through force measurements and particle image velocimetry (PIV), we optimized control parameters to improve braking and turning performances. The results show a 50% reduction in stopping distance, significantly enhancing agility and control. The fin-assisted braking and turning modes enable precise movements, making this approach valuable for autonomous underwater vehicles. This research lays the groundwork for adaptive fin designs and real-time control strategies, with applications in underwater exploration, environmental monitoring, and search-and-rescue operations. Full article

VE-cadherin (VE-cad) membrane stability and localization regulates adhesion formation and actin cytoskeleton dynamics in angiogenesis and vascular remodeling and requires the heparan sulfate proteoglycan (HSPG), Syndecan-4 (Sdc4). This study characterizes the interactions of the heparin receptor, Transmembrane protein-184A (TMEM184A), and Sdc4 in bovine aortic endothelial cells (BAOECs) and the regenerating Zebrafish (ZF) caudal fin and measures the effect of siRNA TMEM184A KD (siTMEM) and TMEM184A overexpression (TMEM OE) on VE-cad levels and localization in confluent and sub-confluent cultured BAOECs. Additionally, we examined the effect of siTMEM on key Rab GTPase trafficking regulators and migrating BAOECs in scratch wound healing assays. We demonstrated that TMEM184A and Sdc4 colocalize in BAOECs and that Sdc4 OE increases colocalization in an HS chain dependent manner, while both Tmem184a and Sdc4 cooperate synergistically in ZF fin angiogenic and tissue repair. We also showed that siTMEM decreases VE-cad membrane and cytoplasmic levels, while increasing scratch wound migration rates. However, TMEM OE cells show increased vesicle formation and VE-cad trafficking and membrane recovery. These findings characterize TMEM184A-Sdc4 cooperation in angiogenesis and indicate a dual function of TMEM184A in signaling and trafficking in vascular cells that promotes VE-cad recovery and membrane localization. Full article

With the continuous scaling of CMOS technology, stress engineering has become increasingly critical at advanced technology nodes, especially in tall and narrow FinFET structures. Asymmetric layout environments (such as dual-Fin structures or poly cuts) can introduce stress imbalance originating from shallow trench isolation (STI), which in turn affects device performance. In this study, TCAD simulations were performed on n-type FinFETs representative of the 10 nm technology node, with a physical gate length of 20 nm, to investigate the correlation between asymmetric stress and device drive current. As the Fin width decreases, the asymmetric stress from STI induces noticeable performance fluctuations, with the mobility enhancement under saturation bias reaching a maximum of 8.42% at W = 6 nm. Similarly, as the Fin body angle deviates from 90° and the Fin top narrows, with W_top = 6 nm and W_bottom = 8 nm, the mobility enhancement peaks at 7.65%. The simulation results confirm that STI-induced asymmetric stress has a significant impact on the Fin sidewall channel, while its effect on the top channel is minimal. To mitigate these effects, CESL stress engineering is proposed as an effective solution to amplify the top channel current, thereby reducing the influence of asymmetric stress on device performance. A CESL stress of 2.0 GPa is shown to improve device stability by approximately 20%. Full article

Vibrational double diffusion has gained increasing attention in recent studies due to its role in enhancing mixing, disrupting thermal boundary layers, and stabilizing convection structures, especially in nanofluids and porous media. This study focuses on the case of two-phase nanofluid flow in the presence of vibrational effects. The flow domain is a fined chamber that is filled with a non-Darcy porous medium. Two concentration formulations are proposed for the species concentration and nanoparticle concentration. The thermal radiation is in both the x- and y-directions, while the flow domain is considered to be inclined. The solution technique depends on an effective finite volume method. The periodic behaviors of the stream function, Nusselt numbers, and Sherwood numbers against the progressing time are presented and interpreted. From the major results, a significant reduction in harmonic behaviors of the stream function is obtained as the lengths of the fins are raised while the gradients of the temperature and concentration are improved. Also, a higher rate of heat and mass transfer is obtained when the vibration frequency is raised. Furthermore, for fixed values of the Rayleigh number and vibration frequency (Ra = 10⁴, σ = 500), the heat transfer coefficient improves by 27.2% as the fin length increases from 0.1 to 0.25. Full article

In the field of fish genetic breeding, accurately determining the DNA content and ploidy of fish is of great significance. This article introduces the use of flow cytometry (FCM) to measure the DNA content and conduct ploidy analysis by sampling different tissues of freshwater fish species. It describes the FCM detection methods and their effectiveness for different individual tissues. These tissues include embryos and fry, as well as the blood, caudal fins, and sperm of adult live fish, and also specific tissues such as testes, ovaries, gills, spleens, and livers under anatomical conditions. Moreover, the application of ploidy detection to different tissues or individuals in different stages in the practice of fish genetic breeding is analyzed. This research covers samples from different growth stages and a variety of tissue types. The results show that this method exhibits high stability and reliability in the detection of different tissue samples, providing solid data support for subsequent research. It holds significant application value in fish genetic breeding. Full article

Financial technology (FinTech) rapidly transforms financial landscapes across ASEAN-4 countries by enhancing financial inclusion and digital service accessibility. However, the key factors driving FinTech development in these economies remain ambiguous. While existing studies highlight the economic and technological aspects of FinTech adoption, limited research distinguishes the unique conditions shaping FinTech’s evolution in developing ASEAN markets. This study bridges this gap by identifying economic and non-economic determinants and exploring their mediating effects. This research aims to investigate the primary drivers of FinTech development in ASEAN-4, emphasizing the roles of financial access and technological readiness as mediators in fostering a sustainable FinTech ecosystem. Utilizing structural equation modeling (SEM) with SmartPLS3, this study analyzes secondary data from 2008 to 2018, evaluating macroeconomic indicators, banking conditions, internet penetration, innovation levels, population dynamics, and human development factors. General banking conditions, access to finance, and technological readiness significantly impact FinTech development. Additionally, financial accessibility and technological infrastructure mediate the influence of economic stability, innovation, and digital penetration on FinTech growth. This study underscores policymakers’ and stakeholders’ need to enhance digital infrastructure and financial accessibility to accelerate FinTech growth. Strengthening financial ecosystems will drive digital transformation and economic resilience in emerging ASEAN economies. Full article

A numerical investigation of the roll motion characteristics of the Basic Finner Model was performed. The study of roll motion is essential in the design and performance evaluation of aerospace vehicles, particularly for stability and maneuverability purposes. The numerical investigation was conducted employing the Unsteady Reynolds-Averaged Navier-Stokes (URANS) solver coupled with k-ε realizable turbulence model. The simulations were performed for a range of Mach numbers and angles of attack to assess the influence of these parameters on the model’s roll motion characteristics. The CFD procedure was validated based on an experimental database from previous work and the literature. The influence of roll motion on aerodynamic forces and moments at different flow conditions were analyzed to obtain a better understanding of the physics. The variation of forces and moments with roll angle, Mach number, and angle of attack, as well as the pressure distribution at different flow conditions, are discussed, also covering aerodynamic interactions between the fins and body. This numerical investigation contributes to understanding the aerodynamic behavior of the Basic Finner Model during roll motion. The findings are valuable for the design and optimization of aerospace vehicles, aiding in the development of more efficient and stable configurations. Future research can be based upon these results to explore additional factors that may impact roll motion characteristics and can further refine the design and performance evaluation processes for aerospace vehicles. Full article

With the increase in trading frequency and the growing complexity of data structures, traditional quantitative strategies have gradually encountered bottlenecks in modeling capacity, real-time responsiveness, and multi-dimensional information integration. To address these limitations, a high-frequency signal generation framework is proposed, which integrates graph neural networks, cross-scale Transformer architectures, and macro factor modeling. This framework enables unified modeling of structural dependencies, temporal fluctuations, and macroeconomic disturbances. In predictive validation experiments, the framework achieved a precision of 92.4%, a recall of 91.6%, and an F1-score of 92.0% on classification tasks. For regression tasks, the mean squared error (MSE) and mean absolute error (MAE) were reduced to $1.76\times {10}^{-4}$ and $0.96\times {10}^{-2}$, respectively. These results significantly outperformed several mainstream models, including LSTM, FinBERT, and StockGCN, demonstrating superior stability and practical applicability. Full article