bluuebunny/crossref_metadata_2025 huggingface dataset:
Multimodal Human–Robot Interaction Using Gestures and Speech: A Case Study for Printed Circuit Board Manufacturing
Authors: Salinas-Martínez Ángel-Gabriel , Cunillé-Rodríguez Joaquín , Aquino-López Elías , García-Moreno Angel-Iván
Published: December 2024

In recent years, technologies for human–robot interaction (HRI) have undergone substantial advancements, facilitating more intuitive, secure, and efficient collaborations between humans and machines. This paper presents a decentralized HRI platform, specifically designed for printed circuit board manufacturing. The proposal incorporates many input devices, including gesture recognition via Leap Motion and Tap Strap, and speech recognition. The gesture recognition system achieved an average accuracy of 95.42% and 97.58% for each device, respectively. The speech control system, called Cellya, exhibited a markedly reduced Word Error Rate of 22.22% and a Character Error Rate of 11.90%. Furthermore, a scalable user management framework, the decentralized multimodal control server, employs biometric security to facilitate the efficient handling of multiple users, regulating permissions and control privileges. The platform’s flexibility and real-time responsiveness are achieved through advanced sensor integration and signal processing techniques, which facilitate intelligent decision-making and enable accurate manipulation of manufacturing cells. The results demonstrate the system’s potential to improve operational efficiency and adaptability in smart manufacturing environments.
Evaluating Energy Efficiency and Optimal Positioning of Industrial Robots in Sustainable Manufacturing
Authors: Ruzarovsky Roman , Horak Tibor , Bocak Robert
Published: December 2024

Optimizing the energy efficiency of robotic workstations is a key aspect of industrial automation. This study focuses on the analysis of the relationship between the position of the robot base and its energy consumption and time aspects. A number of 6-axis robots, including the ABB IRB 120 robot, were investigated in this research by combining measurements and simulations using the energy consumption measurement module in the ABB RobotStudio 2024.1.1 environment. The objective of this study was to develop an energy consumption model that can identify the optimal robot positions to minimize energy costs and time losses. The results suggest that the strategic positioning of the robot has a significant impact on its performance and efficiency. These results demonstrate that the ideal working distance of the robots is approximately 50% of its maximum range, and displacements along the X and Z axes affect the energy and time consumption. These findings suggest the existence of a trade-off between time and energy efficiency, providing a basis for further research into the optimization of robotic systems. Thus, this work offers new perspectives for the design of efficient robotic workstations for cross-sensory applications.
Root Cause Analysis in Industrial Manufacturing: A Scoping Review of Current Research, Challenges and the Promises of AI-Driven Approaches
Authors: Pietsch Dominik , Matthes Marvin , Wieland Uwe , Ihlenfeldt Steffen , Munkelt Torsten
Published: December 2024

The manufacturing industry must maintain high-quality standards while meeting customer demands for customization, reduced carbon footprint, and competitive pricing. To address these challenges, companies are constantly improving their production processes using quality management tools. A crucial aspect of this improvement is the root cause analysis of manufacturing defects. In recent years, there has been a shift from traditional knowledge-driven approaches to data-driven approaches. However, there is a gap in the literature regarding a systematic overview of both methodological types, their overlaps, and the challenges they pose. To fill this gap, this study conducts a scoping literature review of root cause analysis in manufacturing, focusing on both data-driven and knowledge-driven approaches. For this, articles from IEEE Xplore, Scopus, and Web of Science are examined. This review finds that data-driven approaches have become dominant in recent years, with explainable artificial intelligence emerging as a particularly strong approach. Additionally, hybrid variants of root cause analysis, which combine expert knowledge and data-driven approaches, are also prevalent, leveraging the strengths of both worlds. Major challenges identified include dependence on expert knowledge, data availability, and management issues, as well as methodological difficulties. This article also evaluates the potential of artificial intelligence and hybrid approaches for the future, highlighting their promises in advancing root cause analysis in manufacturing.
Effect of the Vortex on the Movement Law of Sand Particles in the Hump Region of Pump-Turbine
Authors: Shi Guangtai , Ye Xunyun , Xiao Yexiang , Guo Xin , Qin Hao , Tang Manqi
Published: December 2024

The pump turbine, as the core equipment of a pumped storage power plant, is most likely to operate in the hump zone between condition changes, which has a great impact on the stable operation of the power plant, and the high sedimentation of a natural river will lead to wear and tear in the overflow components of the equipment. Therefore, this paper is based on the Euler–Lagrange model, and seeks to investigate the distribution of vortices in the hump zone of the pump turbine and its effect on the movement of sand particles. The study shows that as the flow rate increases, the strip vortex in the straight cone section of the draft tube becomes elongated, and the cluster vortex in the elbow tube section gradually decreases. The strip vortex encourages the sand particles to move along its surface, while the cluster vortex hinders the movement of the sand particles. The accumulation areas of the sand particles in the straight cone section and the elbow tube section increase axially and laterally, respectively. The blade vortex in the runner gradually occupies the flow channel as the flow rate increases, and the blade vortex near the pressure surface encourages the sand particles to move towards the suction surface, resulting in the serious accumulation of sand particles on the suction surface. As the flow rate increases, the number of blades where sand particles accumulate increases and the accumulation area moves towards the cover plate and the outlet. The flow separation vortex in the double-row cascade decreases as the flow rate increases, which drives the sand movement in the middle and lower sections of the vanes. The area of sand accumulation in the stay vane decreases with increasing flow rate, but the area of sand accumulation between the guide vanes increases and then decreases. The vortex on the wall surface of the volute gradually decreases with the flow rate, and the vortex zone at the outlet first decreases, then disappears, and finally reappears. The vortex at the wall surface suppresses the sand movement, and its sand accumulation area changes from elongated to lumpy and finally to elongated due to the increase in flow. The results of the study provide an important theoretical reference for reducing the wear of pump turbine overflow components.
Deep Learning-Based Prediction of Pitch Response for Floating Offshore Wind Turbines
Authors: Chen Ruifeng , Zhang Ke , Luo Min , An Ye , Guo Lixiang
Published: December 2024

Accurate dynamic response prediction is a challenging and crucial aspect for the fatigue or ultimate analysis of floating offshore wind turbines (FOWTs), which are increasingly recognized for their potential to harness wind energy in deep-water environments. However, traditional numerical modeling approaches like the finite element method are time-consuming, making them inefficient for generating the extensive datasets required. This paper presents an efficient deep learning-based approach, referred to as the CNN-GRU model, considering multiple external environments. This model integrates convolutional neural networks (CNNs) and gated recurrent units (GRUs), effectively extracting the coupling relationships among various input features and capturing the temporal dependencies to enhance predictive accuracy. The proposed model is applied to two distinct types of FOWTs under three sea states, and the results demonstrate its satisfactory accuracy, with an average correlation coefficient (CC) of 0.9962 and an average coefficient of determination (R²) of 0.9864. The high accuracy across all cases proves the model’s robustness and reliability. Furthermore, the model’s optimal configurations, including memory lengths, sample sizes, and optimizer, are identified through parametric studies. Moreover, the Shapley additive explanations (SHAP) interpretation is utilized to reveal the most significant features influencing structural responses. In addition, a comparative analysis with two other ensemble models, namely random forest and gradient boosting, is conducted. The proposed approach achieves superior accuracy, with computational time approximately half that of the other two models, thereby highlighting its efficiency and effectiveness. The comprehensive framework, which encompasses feature selection, data processing, deep learning model construction, and interpretation, demonstrates significant potential for addressing a broad range of engineering problems through deep learning methodologies.
Enhanced Control Strategies for Underactuated AUVs Using Backstepping Integral Sliding Mode Techniques for Ocean Current Challenges
Authors: Chen Qingdong , Yuan Jianping , Dong Zhihui , Chai Zhuohui , Wan Lei
Published: December 2024

This paper examines the control challenges faced by underactuated Autonomous Underwater Vehicles (AUVs) under ocean current disturbances. It proposes a Backstepping Integral Sliding Mode Control (BISMC) strategy to enhance their adaptability and robustness. The BISMC strategy integrates the system decomposition capability of the backstepping control method with the rapid response and robustness advantages of the Sliding Mode Control method, enabling the design of a heading controller and a double closed-loop depth controller. By introducing an integral component, the strategy eliminates steady-state errors caused by ocean currents, accelerating system convergence and improving accuracy. Furthermore, a saturation function is employed to mitigate output chattering issues. Simulation results demonstrate that the BISMC controller significantly enhances the control precision and anti-disturbance capabilities of AUVs under low-frequency ocean current disturbances, showcasing exceptional adaptive and self-disturbance rejection performance.
On the Hydrodynamic and Structural Performance of Thermoplastic Composite Ship Propellers Produced by Additive Manufacturing Method
Authors: Altunsaray Erkin , Turkmen Serkan , Sözen Ayberk , Doğru Alperen , Liu Pengfei , Halilbeşe Akile Neşe , Neşer Gökdeniz
Published: December 2024

In the marine industry, the search for sustainable methods, materials, and processes, from the product’s design to its end-of-life stages, is a necessity for combating the negative consequences of climate change. In this context, the lightening of products is essential in reducing their environmental impact throughout their life. In addition to lightening through design, lightweight materials, especially plastic-based composites, will need to be used in new and creative ways. The material extrusion technique, one of the additive manufacturing methods, is becoming more widespread day by day, especially in the production of objects with complex forms. This prevalence has not yet been reflected in the marine industry. In this study, the performances of plastic composite propellers produced by the material extrusion technique is investigated and discussed comparatively with the help of both hydrodynamic and structural tests carried out in a cavitation tunnel and mechanical laboratory. The cavitation tunnel test and numerical simulations were conducted at a range of advance coefficients (J) from 0.3 to 0.9. The shaft rate was kept at 16 rps. The thrust and torque data were obtained using the tunnel dynamometer. Digital pictures were taken to obtain structural deformation and cavitation dynamics. The structural performance of the propellers shows that an aluminum propeller is the most rigid, while a short carbon fiber composite propeller is the most flexible. Continuous carbon fiber composite has high strength and stiffness, while continuous glass fiber composite is more cost-effective. In terms of the hydrodynamic performance of the propellers, flexibility reduces the loading on the blade, which can result in thrust and torque reduction. Overall, the efficiency of the composite propellers was similar and less than that of the rigid aluminum propeller. In terms of weight, the composite carbon propeller containing continuous fiber, which is half the weight of the metal propeller, is considered as an alternative to metal in production. These propellers were produced from a unique composite consisting of polyamide, one of the thermoplastics that is a sustainable composite material, and glass and carbon fiber as reinforcements. The findings showed that the manufacturing method and the new composites can be highly successful for producing ship components.
The Main Geohazards in the Russian Sector of the Arctic Ocean
Authors: Krylov Artem A. , Rukavishnikova Daria D. , Novikov Mikhail A. , Baranov Boris V. , Medvedev Igor P. , Kovachev Sergey A. , Lobkovsky Leopold I. , Semiletov Igor P.
Published: December 2024

The Arctic region, including vast shelf zones, has enormous resource and transport potential and is currently key to Russia’s strategic development. This region is promising and attractive for the intensification of global economic activity. When developing this region, it is very important to avoid emergency situations that could result in numerous negative environmental and socio-economic consequences. Therefore, when designing and constructing critical infrastructure facilities in the Arctic, it is necessary to conduct high-quality studies of potential geohazards. This paper reviews and summarizes the scattered information on the main geohazards in the Russian sector of the Arctic Ocean, such as earthquakes, underwater landslides, tsunamis, and focused fluid discharges (gas seeps), and discusses patterns of their spatial distribution and possible relationships with the geodynamic setting of the Arctic region. The study revealed that the main patterns of the mutual distribution of the main geohazards of the Russian sector of the Arctic seas are determined by both the modern geodynamic situation in the region and the history of the geodynamic evolution of the Arctic, namely the formation of the spreading axis and deep-sea basins of the Arctic Ocean. The high probability of the influence of seismotectonic activity on the state of subsea permafrost and massive methane release is emphasized. This review contributes toward better understanding and progress in the zoning of seismic and other geological hazards in the vast Arctic seas of Russia.
Combustion and Emission Characteristics of Methanol–Diesel Dual Fuel Engine at Different Altitudes
Authors: Wang Zhongcheng , Jie Zhu , Liu Xiaoyu
Published: December 2024

Currently, in the two technological approaches for using diesel pilot injection to ignite methanol and partially substituting diesel fuel with methanol, neither can fully achieve carbon neutrality in the context of internal combustion engines. Compression-ignition direct-injection methanol marine engines exhibit significant application potential because of their superior fuel economy and lower carbon emissions. However, the low cetane number of methanol, coupled with its high ignition temperature and latent heat of vaporization, poses challenges, especially amidst increasingly stringent marine emission regulations. It is imperative to comprehensively explore the impacts of the engine geometry, intake boundary conditions, and injection strategies on the engine performance. This paper first investigates the influence of the compression ratio on the engine performance, subsequently analyzes the effects of intake conditions on methanol ignition characteristics, and finally compares the combustion characteristics of the engine under different fuel injection timings. When the compression ratio is set at 13.5, only an injection timing of −30 °CA can initiate methanol compression ignition, but the combustion is not ideal. For compression ratios of 15.5 and 17.5, all the injection timings studied can ignite methanol. Reasonable increases in the intake pressure and intake temperature are beneficial for methanol compression ignition. However, when the intake temperature rises from 400 K to 500 K, a decrease in the thermal efficiency is observed. Particularly, at an injection timing of −30 °CA, both the peak cylinder pressure and peak cylinder temperature are higher, the ignition occurs earlier, the combustion process shifts forward, and the combustion efficiency and indicated thermal efficiency are at higher levels. Furthermore, the overall emissions of NOX, HC, and CO are relatively low. Therefore, selecting an appropriate injection timing is crucial to facilitate the compression ignition and combustion of methanol under low-load conditions.
Removing Ice from Frozen Structures Using Bubble Pulsation Energy
Authors: Song Ying , Zhang Shuai , Wang Shiping , Kang Zhuang
Published: December 2024

Icing poses significant threats to the safety and reliability of structures in cold regions, thus prompting researchers to explore deicing methods. This paper establishes a bubble deicing system and investigates the utilization of bubble pulsation energy for removing ice from frozen structures. Traditional deicing methods suffer from issues such as high energy consumption, severe environmental pollution, and secondary icing. To address these challenges and advance the development of deicing technology, harnessing the substantial energy generated by bubble pulsation can be employed for effective deicing purposes. Through experimental analysis, this study successfully demonstrates the feasibility of employing cavitation for deicing applications. Several experimental cases are conducted to elucidate the mechanism behind bubble-energy-based deicing, varying parameters including the distance between bubbles and targets as well as ice sheet thickness within a range of 5~35 mm. The findings indicate that bubbles can effectively facilitate deicing processes. Further exploration is required to fully understand the potential of bubble deicing technology and its prospects in engineering applications.
A LES-ALM Study for the Turbulence Characteristics of Wind Turbine Wake Under Different Roughness Lengths
Authors: Liu Guangyi , Yang Qingshan
Published: December 2024

To investigate the characteristics of wind turbine wakes under different aerodynamic roughness lengths, a series of LES-ALM simulations were carried out in this study. First, a sensitivity analysis of the time step of the simulation results was performed. Then, the study compared the power and thrust of wind turbines under different roughness conditions. Finally, the mean velocity deficit, added turbulence intensity, and Reynolds shear stresses in the wake were analyzed under different roughness conditions. This study finds that a 0.1 s time step can provide satisfactory results for the LES-ALM compared to a 0.02 s time step. Furthermore, for the same hub-height wind speed, the thrust coefficient varies from 0.75 to 0.8 under the different roughness levels. As the roughness length increases, the time-averaged velocity deficit and added turbulence intensity decreases, and the wake recovers more quickly at the incoming level. However, the effect of roughness length on the Reynolds shear stress is weak within the downstream range of x = 6D to 10D. For the velocity deficit, a single Gaussian function is not able to describe its vertical distribution. Additionally, under higher roughness conditions, the height of the wake center is distinctively higher than the hub height as the wake develops downstream. The findings of this paper are beneficial for selecting the approximate numerical parameters for the wake simulations and provide deeper insights into the turbulence mechanisms of wind turbine wake, which are crucial for establishing analytical models to predict the wake field.
Impact of International Oil Price Shocks and Inflation on Bank Efficiency and Financial Stability: Evidence from Saudi Arabian Banking Sector
Authors: Bouzidi Fathi Mohamed , Nefzi Aida Arbi , Al Yousif Mohammed
Published: December 2024

This study examines the short-run and long-run equilibrium relationship between the banking sector’s efficiency and stability and its endogenous and exogenous determinants, such as inflation and international oil price shocks in Saudi Arabia from 2004 to 2022. This study differentiates between the direct and indirect effects of international oil price changes on bank efficiency and stability and investigates how these changes can affect the banking sector through inflation. The first stage uses a panel Autoregressive Distributive Lag (ARDL). The empirical result confirms a long/short-run relationship between oil price shocks and the stability and efficiency of banks. In the long run, the relationship is statistically significant and positive, and it is negative in the short run. On the other hand, this study finds that oil price shocks directly affect the stability and efficiency of banks. In the second stage, this study uses a nonlinear ARD (NARD) to examine the short- and long-run asymmetric impacts of oil price shocks on the stability and efficiency of banks by decomposing the oil price index into positive and negative changes. The findings confirm an asymmetric relationship between oil prices and the stability and efficiency of banks in Saudi Arabia. In addition, a positive change in oil price can affect the stability and efficiency of banks more than a negative one. Overall, the findings highlight the need for policymakers in Saudi Arabia to be vigilant in addressing potential risks arising from oil price fluctuations and to adopt appropriate policy measures to maintain stability and efficiency in the banking sector.
Construction and Evaluation of Urban Green Infrastructure in Ecologically Vulnerable Areas Based on Multi-Scale and Multi-Objective Approaches: Taking the Four-Lake Hydrographic Network in the Upper Yellow River Basin as an Example
Authors: Cheng Wenhao , Ma Caihong
Published: December 2024

The construction and optimization of urban green infrastructure (UGI) are regarded as effective strategies for harmonizing the natural landscape with human activities, particularly in ecologically vulnerable areas in the Upper Yellow River Basin, China. However, there is little attention paid to the scale effects and object effects as well as an absence of comprehensive assessments regarding landscape stability. Taking the Four-Lake Hydrographic Network (FLHN) in Shizuishan, a prefecture-level city, as an example, this study focuses on identifying the important sources of UGI by integrating both regional and interregional perspectives utilizing morphological spatial pattern analysis (MSPA). UGI networks were constructed and optimized based on trade-offs and synergizing relationships between individual objects using the minimum cumulative resistance (MCR) model, and the UGI network’s stability combined centrality and connectivity aspects, which were subsequently assessed. The results showed that a total of 19 important sources covering an area of 105.07 km2 were identified in the FLHN, integrating both regional and interregional levels. It was deemed unnecessary to maintain lengths of 7.79 km key corridors, 9.42 km general corridors, and 29.89 km fragile corridors; furthermore, there was no longer a requirement to upgrade an additional 5.51 km of general corridors and 25.78 km of any corridor, as UGI corridors were extracted based on a trade-off and synthesized objective methodology. The overall connectivity index value (OG) of UGI stability with respect to the multi-objective model demonstrated superior performance compared to the same index in scenarios involving the use of a single-objective approach and the straightforward overlay of each object. This study reveals the multifaceted requirements of urban landscape security and sustainability, indicating that multi-scale and multi-objective approaches in territorial space planning not only ensure the integrity of the landscape patterns but also reduce the costs associated with landscape construction. This model can be utilized to implement urban landscape entity protection and restoration for landscapes with various geographical characteristics, and it can provide valuable guidance for similar areas.
Assessing and Predicting Spatiotemporal Alterations in Land-Use Carbon Emission and Its Implications to Carbon-Neutrality Target: A Case Study of Beijing-Tianjin-Hebei Region
Authors: Lv Weitong , Xie Yongqing , Zeng Peng
Published: December 2024

Optimizing land use and management are pivotal for mitigating land use-related carbon emissions. Current studies are less focused on the influence of development policies and spatial planning on carbon emissions from land use. This research employs the future land use simulation (FLUS) model to project land-use alterations under the business-as-usual (BAU) and low-carbon ecological security (LCES) scenarios. It assesses and predicts spatiotemporal characteristics of land-use carbon emissions in the Beijing-Tianjin-Hebei (BTH) region across urban agglomerations, cities, counties, and grids from 2000 to 2030. The influence of low-carbon policy is assessed by comparing the land-use carbon emissions between scenarios. The findings demonstrate that: (1) Urban agglomeration-wise, Beijing’s land-use carbon emissions and intensities peaked and declined, while Tianjin and Hebei’s continued to rise. (2) City-wise, central urban areas generally have higher carbon emissions intensities than non-central areas. (3) County-wise, in 2030, high carbon-intensity counties cluster near development axes. Still, the BAU scenario has a larger carbon emission intensity and a greater range of higher intensities. (4) Grid-wise, in 2030, the BAU scenario shows a clear substitution of heavy carbon emission zones for medium ones, and the LCES scenario shows a clear substitution of carbon sequestration zones for light carbon emission zones. Our methodology and findings can optimize spatial planning and carbon reduction policies in the BTH urban agglomeration and similar contexts.
Comparing Measuring Methods for Speech Timing During Rapid Automatic Naming Tasks Using Arabic and English Bilingual Adults
Authors: Morris Richard J. , Tibi Sana , Alsalmi Hany M. , Latham Bailey
Published: December 2024

Rapid automatized naming (RAN) tasks have been used to assess word retrieval speed and reading speed. However, measures of reading rate may be affected by structural differences between languages. This study was designed to measure talking speed differences in the utterances of a group of speakers whose first language (L1) was Arabic and second language (L2) was English. Three hypotheses were tested: the language structure differences would result in L2 productions with slower utterance rates; the L2 speaking rate would be characterized by greater utterance interruptions than the L1; and the articulation rate would more accurately indicate the naming speeds of the participants. Twenty Arabic- and English-speaking young adults completed RAN tasks for number and object naming in both languages. Utterance rate, speaking rate, and articulation rate were measured. The participants exhibited similar utterance durations in the two languages and utterance rate in words per second, but faster utterance rates in Arabic when measuring the utterance rate in syllables per second. They exhibited more pauses and longer pauses during the English productions, particularly for the object naming. Finally, the speaking rate in syllables per second and articulation rate in syllables per second were equivalent as utterance rates.
Investigating Adult Learners’ Perceptual and Phonolexical Representations of Novel Phonological Contrasts
Authors: Barrios Shannon L. , Hayes-Harb Rachel , Moffatt Joanne C.
Published: December 2024

Previous studies have shown that language learners’ auditory word recognition behavior provides evidence for independent contributions of perceptual and phonolexical representations, and learners’ patterns of auditory word recognition have been characterized as resulting from “fuzziness” or “imprecision” associated with these representations. More recently, it has been argued that representational “fuzziness” may in fact take various forms (e.g., neutralized, precise, ambiguous). The purpose of the present study is to further build on this line of work by elaborating additional logically possible scenarios by crossing larger sets of logically possible types of perceptual and phonolexical representational precision/imprecision, as an exercise in exploring the empirical and theoretical implications of our characterizations of representational fuzziness in language learners. We collect new empirical data for the purpose of demonstrating how we might evaluate auditory word recognition performance relative to this fuller set of predicted scenarios. We computed the set of hypothesized scenarios by crossing possible perceptual and lexical representations. We crossed four possible perceptual representations (NeutralizedC + NeutralizedV, NeutralizedC + PreciseV, PreciseC + NeutralizedV, or PreciseC + PreciseV) and six possible phonolexical representations (Neutralized, Ambiguous, Not X, Precise, Fuzzy Word, or Word Length), for a total of 24 scenarios, each accompanied by a set of predictions with respect to accuracy on an auditory word–picture matching test. We interpret the group and individual performance relative to these scenarios with the ultimate aim of better understanding the implications of our assumptions about the nature of perceptual and phonolexical representations relative to observed patterns of learner behavior. Our hope is that in computing this factorial typology of logically possible scenarios and demonstrating a starting point for how we might empirically evaluate its predictions, we set the stage for future research to refine the hypothesis space through empirical studies of auditory word processing in language learners.
Leveraging Bayesian Quadrature for Accurate and Fast Credit Valuation Adjustment Calculations
Authors: Lehdili Noureddine , Oswald Pascal , Mirinioui Othmane
Published: December 2024

Counterparty risk, which combines market and credit risks, gained prominence after the 2008 financial crisis due to its complexity and systemic implications. Traditional management methods, such as netting and collateralization, have become computationally demanding under frameworks like the Fundamental Review of the Trading Book (FRTB). This paper explores the combined application of Gaussian process regression (GPR) and Bayesian quadrature (BQ) to enhance the efficiency and accuracy of counterparty risk metrics, particularly credit valuation adjustment (CVA). This approach balances excellent precision with significant computational performance gains. Focusing on fixed-income derivatives portfolios, such as interest rate swaps and swaptions, within the One-Factor Linear Gaussian Markov (LGM-1F) model framework, we highlight three key contributions. First, we approximate swaption prices using Bachelier’s formula, showing that forward-starting swap rates can be modeled as Gaussian dynamics, enabling efficient CVA computations. Second, we demonstrate the practical relevance of an analytical approximation for the CVA of an interest rate swap portfolio. Finally, the combined use of Gaussian processes and Bayesian quadrature underscores a powerful synergy between precision and computational efficiency, making it a valuable tool for credit risk management.
Galerkin Finite Element Method for Caputo–Hadamard Time-Space Fractional Diffusion Equation
Authors: Zhao Zhengang , Zheng Yunying
Published: December 2024

In this paper, we study the Caputo–Hadamard time-space fractional diffusion equation, where the Caputo derivative is defined in the temporal direction and the Hadamard derivative is defined in the spatial direction separately. We first use the Laplace transform and the modified Fourier transform to study the analytical solution of the Cauchy problem. Then, using the Galerkin finite element method in space, we generate a semi-discrete scheme and study the convergence analysis. Furthermore, using the L1 scheme of the Caputo derivative in time, we construct a fully discrete scheme and then discuss the stability and error estimation in detail. Finally, the numerical experiments are displaced to verify the theoretical results.
Volatility Spillover Between the Carbon Market and Traditional Energy Market Using the DGC-t-MSV Model
Authors: Wang Jining , Zeng Renjie , Wang Lei
Published: December 2024

This study employed the dynamic conditional correlation algorithm and incorporated the temporal dynamics of spillover effect to enhance the Multivariate Stochastic Volatility (MSV) model. Consequently, a DGC-t-MSV model (multiple stochastic volatility model of dynamic correlation coefficient with Granger causality test) was constructed to simulate and examine the volatility spillover effects between China’s carbon market and the traditional energy market. The findings reveal the following: (1) A significant spillover effect in price volatility exists between China’s carbon and traditional energy markets, with a notably fluctuating spillover index. The traditional energy market in China exerts a stronger unidirectional volatility spillover effect on the carbon market. Price fluctuations in the traditional energy market impact carbon market prices through mechanisms such as cost transmission and market expectations. (2) In the initial stages, the dynamic correlation between China’s carbon and traditional energy markets showed an overall downward trend, underscoring the positive influence of policy incentives and technological advancements on the growth of alternative energy. A mutual weakening effect exists between the carbon and traditional energy markets. (3) Price fluctuations in China’s carbon and traditional energy markets display a high degree of interdependence and short-term persistence, with evidence of a long memory and significant inertia in these price movements. Integration of the DGC-t-MSV model with the Bayesian approach and the Markov Chain Monte Carlo (MCMC) method and the introduction of a time-varying factor enabled the efficient measurement of the volatility spillover effect between China’s carbon and traditional energy markets.
Decision Making by Trade-In Programs in the E-Commerce Supply Chain, Considering Cash Rebate Strategies and Platform Services
Authors: Yu Zhaoqing , Wang Yuyan , Shen Liang , Ji Xue
Published: December 2024

As the consumer market becomes more saturated, the e-commerce supply chain (E-SC) has introduced the trade-in program in an attempt to drive consumption. This paper considers the strategy of the E-SC to provide cash rebates for consumers participating in the trade-in program and incorporates the e-commerce platform’s trade-in service efforts into the E-SC’s decision-making system. Depending on who provides the cash rebate, we construct four decision-making models of the E-SC, i.e., the no-cash-rebate model, the manufacturer model, the platform model, and the cooperative model, where both the manufacturer and the platform jointly provide the cash rebate. We show that the platform model reduces the trade-in service level, but the manufacturer model increases the trade-in service level. In addition, since the cash rebate increases operation costs, the sale price of products is inevitably improved. Furthermore, the platform model raises product demand, but the manufacturer model lowers product demand. The cooperative model proves effective in enhancing demand only when the manufacturer contributes a minor share of the cash rebate and the trade-in service is less efficient. Cash rebates can increase the E-SC’s profits, but the degree of this increase becomes smaller as the cash rebates increase. The manufacturer and the platform always want to exploit each other’s cash rebate strategies. Consumer surplus and social welfare are highest in the platform model and lowest in the manufacturer model. Taking into account the profits, consumer surplus, and social welfare, the platform model is the most conducive to E-SC system operations.
Modified Wave-Front Propagation and Dynamics Coming from Higher-Order Double-Well Potentials in the Allen–Cahn Equations
Authors: Kim Junseok
Published: December 2024

In this paper, we conduct a numerical investigation into the influence of polynomial order on wave-front propagation in the Allen–Cahn (AC) equations with high-order polynomial potentials. The conventional double-well potential in these equations is typically a fourth-order polynomial. However, higher-order double-well potentials, such as sixth, eighth, or any even order greater than four, can model more complex dynamics in phase transition problems. Our study aims to explore how the order of these polynomial potentials affects the speed and behavior of front propagation in the AC framework. By systematically varying the polynomial order, we observe significant changes in front dynamics. Higher-order polynomials tend to influence the sharpness and speed of moving fronts, leading to modifications in the overall pattern formation process. These results have implications for understanding the role of polynomial potentials in phase transition phenomena and offer insights into the broader application of AC equations for modeling complex systems. This work demonstrates the importance of considering higher-order polynomial potentials when analyzing front propagation and phase transitions, as the choice of polynomial order can dramatically alter system behavior.
GRUvader: Sentiment-Informed Stock Market Prediction
Authors: Mamillapalli Akhila , Ogunleye Bayode , Timoteo Inacio Sonia , Shobayo Olamilekan
Published: December 2024

Stock price prediction is challenging due to global economic instability, high volatility, and the complexity of financial markets. Hence, this study compared several machine learning algorithms for stock market prediction and further examined the influence of a sentiment analysis indicator on the prediction of stock prices. Our results were two-fold. Firstly, we used a lexicon-based sentiment analysis approach to identify sentiment features, thus evidencing the correlation between the sentiment indicator and stock price movement. Secondly, we proposed the use of GRUvader, an optimal gated recurrent unit network, for stock market prediction. Our findings suggest that stand-alone models struggled compared with AI-enhanced models. Thus, our paper makes further recommendations on latter systems.
A Proof Calculus for Automated Deduction in Propositional Product Logic
Authors: Guller Dušan
Published: December 2024

Propositional product logic belongs to the basic fuzzy logics with continuous t-norms using the product t-norm (defined as the ordinary product of real numbers) on the unit interval [0,1]. This paper introduces a proof calculus for the product logic which is suitable for automated deduction. The calculus provides one of possible generalisations of the family of modifications of the procedure (algorithm) of Davis, Putnam, Logemann, and Loveland (DPLL) in the context of fuzzy logics. We show that the calculus is refutation sound and finitely complete as well. The deduction, satisfiability, and validity problems are solved in the finite case. The achieved results contribute to the theoretical (logic and computational) description of multi-step fuzzy inference.
Numerical Simulation for the Wave of the Variable Coefficient Nonlinear Schrödinger Equation Based on the Lattice Boltzmann Method
Authors: Wang Huimin , Chen Hengjia , Li Ting
Published: December 2024

The variable coefficient nonlinear Schrödinger equation has a wide range of applications in various research fields. This work focuses on the wave propagation based on the variable coefficient nonlinear Schrödinger equation and the variable coefficient fractional order nonlinear Schrödinger equation. Due to the great challenge of accurately solving such problems, this work considers numerical simulation research on this type of problem. We innovatively consider using a mesoscopic numerical method, the lattice Boltzmann method, to study this type of problem, constructing lattice Boltzmann models for these two types of equations, and conducting numerical simulations of wave propagation. Error analysis was conducted on the model, and the convergence of the model was numerical validated. By comparing it with other classic schemes, the effectiveness of the model has been verified. The results indicate that lattice Boltzmann method has demonstrated advantages in both computational accuracy and time consumption. This study has positive significance for the fields of applied mathematics, nonlinear optics, and computational fluid dynamics.
On the Dynamics of Some Higher-Order Nonlinear Difference Equations
Authors: Alharbi Turki D. , Hasan Md Rifat
Published: December 2024

This research investigates the dynamics of higher-order nonlinear difference equations, specifically concentrating on seventh-order instances. Analytical solutions are obtained for particular equations, a formidable task owing to the absence of explicit mathematical techniques for their resolution. The qualitative characteristics of solutions, such as their stability, boundedness, and periodicity, are analysed by theoretical methods and numerical simulations. The results indicate that equilibrium points frequently lack local asymptotic stability, leading to intricate phenomena such as unbounded solutions and periodic attractors. These findings augment our understanding of nonlinear difference equations, offering significant implications for their use across various scientific fields.
Evolution and Motivation of the Value-Added Trade Pattern of Producer Services Based on a Complex Network
Authors: Li Yan , Liang Xuehan , Guan Sizhe , Huang Qingbo
Published: December 2024

With the acceleration of the digital revolution and free trade, the producer services industry has become a new driving force for countries to enhance their global value chain (GVC). The further integration of GVCs has promoted the formation of a global value network covering a wider range of regions and industries. The way of establishing strong core capabilities and competitive advantages for a country’s producer services industry in the vast value network is of great practical significance for achieving industrial structure transformation and upgrading in an open economy. Therefore, based on the Asian Development Bank Multi-Regional Input–Output Database (ADB-MRIO) and the bilateral export value-added accounting method (WWZ), this study constructs a value network of producer services and analyzes its structural evolution and driving factors. The results show the following: (1) The domestic value-added (DVA) network and foreign value-added (FVA) networks of producer services trade exhibit dense characteristics in terms of trade depth. The DVA network has a small-world topological structure, while the FVA network does not have this feature most years. (2) Western countries, represented by the USA, Germany, and the UK, are located at the hub of the global value network, while China’s network status is rising and gradually occupying a core position not only in the Asian region but also in the world. The value chain of producer services shows a pattern of being led by developed countries and extending to developing countries. (3) The broader tendency of modularity is decreasing, indicating that the development process of the DVA and FVA networks is becoming globalized. The members of the community can not only achieve trade interdependence due to geographical proximity but also break through regional limitations and complement each other. (4) The network evolution process is driven by a variety of factors. Industrial and technological distances are conducive to the expansion of the global value flow in producer services. Geographical and linguistic proximity are the main influencing factors, while the role of institutional proximity tends to be weakened.
Cardiovascular Diseases in Public Health: Chromosomal Abnormalities in Congenital Heart Disease Causing Sudden Cardiac Death in Children
Authors: Salzillo Cecilia , La Verde Marco , Imparato Amalia , Molitierno Rossella , Lucà Stefano , Pagliuca Francesca , Marzullo Andrea
Published: December 2024

Chromosomal abnormalities (CAs) are changes in the number or structure of chromosomes, manifested as alterations in the total number of chromosomes or as structural abnormalities involving the loss, duplication, or rearrangement of chromosomal segments. CAs can be inherited or can occur spontaneously, leading to congenital malformations and genetic diseases. CAs associated with cardiovascular diseases cause structural or functional alterations of the heart, affecting the cardiac chambers, valves, coronary arteries, aorta, and cardiac conduction, thus increasing the likelihood of arrhythmias, cardiac arrest, and sudden cardiac death (SCD). An early diagnosis and the adequate management of chromosomal abnormalities associated with cardiovascular diseases are essential to prevent SCD, which is a serious public health problem today. In our review, we analyzed the structural and functional CAs responsible for congenital heart disease (CHD) that increase the risk of SCD and analyzed the prevention strategies to be implemented to reduce SCD.
Circulating B Lymphocyte Subsets in Patients with Systemic Lupus Erythematosus
Authors: Kosałka-Węgiel Joanna , Jakieła Bogdan , Dziedzic Radosław , Milewski Mamert , Siwiec-Koźlik Andżelika , Zaręba Lech , Bazan-Socha Stanisława , Sanak Marek , Musiał Jacek , Korkosz Mariusz
Published: December 2024

Background/Objectives: Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by the abnormal activation of autoreactive T and B cells, autoantibody production, complement activation, and immune-complex deposition, resulting in tissue damage. However, data on immunologic disturbances in SLE, particularly regarding flares, are scarce. Methods: We investigated 35 patients with SLE: 12 (34.3%) with disease exacerbation (SLE disease activity index [SLEDAI] ≥ 5 points) and 23 (65.7%) in remission (SLEDAI < 5 points). All patients met the 2019 EULAR/ACR SLE criteria. Flow cytometry was used to identify B cell subsets, including memory B cells. Results: In the whole patient group, SLEDAI was positively related to the percentage of transitional/regulatory B cells (r = 0.38, p = 0.034). Some lymphocyte subsets correlated with complement levels, e.g., the percentage of naïve and memory B cells showed associations with C3c complement (r = 0.43, p = 0.018 and r = −0.45, p = 0.016, respectively). Furthermore, regarding inflammatory markers, we found associations between C-reactive protein and the percentage of plasmablasts (r = 0.40, p = 0.026) and plasmocytes (r = 0.44, p = 0.017). Finally, the percentage of plasmablasts correlated with SLE duration (r = 0.42, p = 0.016). In the follow-up analysis, during a median observation of 5 years, 5 out of the initially 23 inactive SLE patients developed a disease flare. They were characterized by longer disease duration stated in the beginning compared to patients who remained in remission (p = 0.019). Conclusions: Our study highlights significant associations between various B cell subsets and SLE disease activity. A more personalized approach to indicate patients with SLE at a higher risk of lupus flares is crucial for better management.