Search Results (143)

Optical solitons have emerged as a highly active research domain in nonlinear fiber optics, driving significant advancements and enabling a wide range of practical applications. This study investigates the dynamics of dark solitons in systems governed by the resonant nonlinear Schrödinger equation (RNLSE). For the RNLSE with third-order (3OD) and fourth-order (4OD) dispersions, the dark soliton solution of the equation in the (1+1)-dimensional case is derived using the F-expansion method, and the analytical study is extended to the (2+1)-dimensional case via the self-similar method. Subsequently, the nonlinear equation incorporating perturbation terms is further studied, with particular attention given to the dark soliton solutions in both one and two dimensions. The soliton dynamics are illustrated through graphical representations to elucidate their propagation characteristics. Finally, modulation instability analysis is conducted to evaluate the stability of the nonlinear system. These theoretical findings provide a solid foundation for experimental investigations of dark solitons within the systems governed by the RNLSE model. Full article

Edge detection, a cornerstone of computer vision, identifies intensity discontinuities in images, enabling applications from object recognition to autonomous navigation. This survey presents a mathematically grounded analysis of edge detection’s evolution, spanning traditional gradient-based methods, convolutional neural networks (CNNs), attention-driven architectures, transformer-backbone models, and generative paradigms. Beginning with Sobel and Canny’s kernel-based approaches, we trace the shift to data-driven CNNs like Holistically Nested Edge Detection (HED) and Bidirectional Cascade Network (BDCN), which leverage multi-scale supervision and achieve ODS (Optimal Dataset Scale) scores 0.788 and 0.806, respectively. Attention mechanisms, as in EdgeNAT (ODS 0.860) and RankED (ODS 0.824), enhance global context, while generative models like GED (ODS 0.870) achieve state-of-the-art precision via diffusion and GAN frameworks. Evaluated on BSDS500 and NYUDv2, these methods highlight a trajectory toward accuracy and robustness, yet challenges in efficiency, generalization, and multi-modal integration persist. By synthesizing mathematical formulations, performance metrics, and future directions, this survey equips researchers with a comprehensive understanding of edge detection’s past, present, and potential, bridging theoretical insights with practical advancements. Full article

This review surveys recent advances and emerging prospects in phase-transfer catalysis (PTC) for fuel desulfurization. In response to increasingly stringent environmental regulations, the removal of sulfur from transportation fuels has become imperative for curbing SO_x emissions. Conventional hydrodesulfurization (HDS) operates under severe temperature–pressure conditions and displays limited efficacy toward sterically hindered thiophenic compounds, motivating the exploration of non-hydrogen routes such as oxidative desulfurization (ODS). Within ODS, PTC offers distinctive benefits by shuttling reactants across immiscible phases, thereby enhancing reaction rates and selectivity. In particular, PTC enables efficient migration of organosulfur substrates from the hydrocarbon matrix into an aqueous phase where they are oxidized and subsequently extracted. The review first summarizes the deployment of classic PTC systems—quaternary ammonium salts, crown ethers, and related agents—in ODS operations and then delineates the underlying phase-transfer mechanisms, encompassing reaction-controlled, thermally triggered, photo-responsive, and pH-sensitive cycles. Attention is next directed to a new generation of catalysts, including quaternary-ammonium polyoxometalates, imidazolium-substituted polyoxometalates, and ionic-liquid-based hybrids. Their tailored architectures, catalytic performance, and mechanistic attributes are analyzed comprehensively. By incorporating multifunctional supports or rational structural modifications, these systems deliver superior desulfurization efficiency, product selectivity, and recyclability. Despite such progress, commercial deployment is hindered by the following outstanding issues: long-term catalyst durability, continuous-flow reactor design, and full life-cycle cost optimization. Future research should, therefore, focus on elucidating structure–performance relationships, translating batch protocols into robust continuous processes, and performing rigorous environmental and techno-economic assessments to accelerate the industrial adoption of PTC-enabled desulfurization. Full article

This study focuses on the analysis of soliton solutions within the framework of the time-fractional cubic–quintic nonlinear Schrödinger equation (TFCQ-NLSE), a powerful model with broad applications in complex physical phenomena such as fiber optic communications, nonlinear optics, optical signal processing, and laser–tissue interactions in medical science. The nonlinear effects exhibited by the model—such as self-focusing, self-phase modulation, and wave mixing—are influenced by the combined impact of the cubic and quintic nonlinear terms. To explore the dynamics of this model, we apply a robust analytical technique known as the sub-ODE method, which reveals a diverse range of soliton structures and offers deep insight into laser pulse interactions. The investigation yields a rich set of explicit soliton solutions, including hyperbolic, rational, singular, bright, Jacobian elliptic, Weierstrass elliptic, and periodic solutions. These waveforms have significant real-world relevance: bright solitons are employed in fiber optic communications for distortion-free long-distance data transmission, while both bright and dark solitons are used in nonlinear optics to study light behavior in media with intensity-dependent refractive indices. Solitons also contribute to advancements in quantum technologies, precision measurement, and fiber laser systems, where hyperbolic and periodic solitons facilitate stable, high-intensity pulse generation. Additionally, in nonlinear acoustics, solitons describe wave propagation in media where amplitude influences wave speed. Overall, this work highlights the theoretical depth and practical utility of soliton dynamics in fractional nonlinear systems. Full article

Passion fruit (Passiflora edulis Sims), belonging to the Passifloraceae family, is an economically important plant in tropical and subtropical regions. The advances in functional genomics research of passion fruit have been significantly hindered by its recalcitrance to regeneration and stable transformation. This study establishes the first efficient Agrobacterium rhizogenes-mediated hairy root transformation system for passion fruit. Utilizing the eGFP marker gene, transformation efficiencies of 11.3% were initially achieved with strains K599, MSU440, and C58C1, with K599 proving most effective. Key transformation parameters were systematically optimized to achieve the following: OD₆₀₀ = 0.6, infection duration 30 min, acetosyringone concentration 100 μM, and a dark co-cultivation period of 2 days. The system’s utility was further enhanced by incorporating the red visual marker RUBY, enabling direct, instrument-free identification of transgenic roots via betaxanthin accumulation. Finally, this system was applied for functional analysis using PeMYB123, which may be involved in proanthocyanidin accumulation. Overexpression of PeMYB123 produced a higher content of proanthocyanidin in hairy roots. Additionally, the PeANR gene involved in the proanthocyanidin pathway was strongly activated in the transgenic hairy roots. This rapid and efficient visually simplified hairy root transformation system provides a powerful tool for functional gene studies in passion fruit. Full article

Mathematical modeling plays a crucial role in supporting decision-making across a wide range of scientific disciplines. These models often involve multiple parameters, the estimation of which is critical to assessing their reliability and predictive power. Recent advancements in artificial intelligence have made it possible to efficiently estimate such parameters with high accuracy. In this study, we focus on modeling the dynamics of cryptocurrency market shares by employing a Lotka–Volterra system. We introduce a methodology based on a deep neural network (DNN) to estimate the parameters of the Lotka–Volterra model, which are subsequently used to numerically solve the system using a fourth-order Runge–Kutta method. The proposed approach, when applied to real-world market share data for Bitcoin, Ethereum, and alternative cryptocurrencies, demonstrates excellent alignment with empirical observations. Our method achieves RMSEs of 0.0687 (BTC), 0.0268 (ETH), and 0.0558 (ALTs)—an over 50% reduction in error relative to ARIMA(2,1,2) and over 25% relative to a standard NN–ODE model—thereby underscoring its effectiveness for cryptocurrency-market forecasting. The entire framework, including neural network training and Runge–Kutta integration, was implemented in MATLAB R2024a (version 24.1). Full article

The establishment of a transient expression system in petals is significant for elucidating gene functions in flowering trees characterized by a prolonged juvenile phase. Genetic improvements in Camellia japonica have been hindered due to the absence of a functional validation platform. In this study, we explored an Agrobacterium-mediated and readily observable transient expression system in camellia petals to systematically optimize four critical factors affecting transformation efficiency. As a result, the bud stage, ‘Banliuxiang’ genotype, OD600 of 1.0, and 1-day co-cultivation achieved the highest intensity of transient expression, and overexpression of the Ruby1 reporter gene induced substantial anthocyanin synthesis, manifested as distinct red pigmentation. Furthermore, the optimized transient expression system revealed that the R2R3-MYB transcription factor CjMYB5, which interacted with CjGL3, promoted anthocyanin biosynthesis in camellia petals by transactivating key DFR structural genes. This transient expression platform not only advances functional genomics studies in ornamental woody species but also lays a foundation for molecular breeding programs in C. japonica. Full article

With the advancement of intelligent transportation systems, vehicle trajectory data have become a key component in areas like traffic flow prediction, route planning, and traffic management. However, high-quality, publicly available trajectory datasets are scarce due to concerns over privacy, copyright, and data collection costs. The lack of data creates challenges for training machine learning models and optimizing algorithms. To address this, we propose a new method for generating synthetic vehicle trajectory data, leveraging traffic flow characteristics and road maps. The approach begins by estimating hourly traffic volumes, then it uses the Poisson distribution modeling to assign departure times to synthetic trajectories. Origin and destination (OD) distributions are determined by analyzing historical data, allowing for the assignment of OD pairs to each synthetic trajectory. Path planning is then applied using a road map to generate a travel route. Finally, trajectory points, including positions and timestamps, are calculated based on road segment lengths and recommended speeds, with noise added to enhance realism. This method offers flexibility to incorporate additional information based on specific application needs, providing valuable opportunities for machine learning in intelligent transportation systems. Full article

As urban traffic systems become increasingly complex, traditional traffic data collection methods based on fixed detectors face challenges such as poor data quality and acquisition difficulties. Traditional methods also lack the ability to capture complete vehicle path information essential for signal optimization. While vehicle trajectory data can provide rich spatiotemporal information, its sampling characteristics present new technical challenges for traffic parameter extraction. This study addresses the key issue of extracting traffic parameters suitable for signal timing optimization from sampled trajectory data by proposing a comprehensive method for traffic parameter extraction and analysis based on vehicle trajectory data. The method comprises five modules: data preprocessing, basic feature processing, exploratory data analysis, key feature extraction, and data visualization. An innovative algorithm is proposed to identify which intersections vehicles pass through, effectively solving the challenge of mapping GPS points to road network nodes. A dual calculation method based on instantaneous speed and time difference is adopted, improving parameter estimation accuracy through multi-source data fusion. A highly automated processing toolchain based on Python and MATLAB is developed. The method advances the state of the art through a novel polygon-based trajectory mapping algorithm and a systematic multi-source parameter extraction framework specifically designed for signal control optimization. Validation using actual trajectory data containing 2.48 million records successfully eliminated 30.80% redundant data and accurately identified complete paths for 7252 vehicles. The extracted multi-dimensional parameters, including link flow, average speed, travel time, and OD matrices, accurately reflect network operational status, identifying congestion hotspots, tidal traffic characteristics, and unstable road segments. The research outcomes provide a feasible technical solution for areas lacking traditional detection equipment. The extracted parameters can directly support signal optimization applications such as traffic signal coordination, timing optimization, and congestion management, providing crucial support for implementing data-driven intelligent traffic control. This research presents a theoretical framework validated with real-world data, providing a foundation for future implementation in operational signal control systems. Full article

Fresh-cut potatoes are highly perishable, requiring effective preservation strategies to maintain quality and extend shelf life. This study evaluated the use of edible coatings and the combination of osmotic dehydration and ohmic heating (OH-OD), both integrated with modified atmosphere packaging (MAP), to enhance microbial stability and reduce quality deterioration. Key quality parameters—including color stability, browning index, weight loss, microbial activity, and sensory attributes—were assessed. Results showed that coated samples (E-FP) had the lowest browning index (59.71) by day 8, compared to a value of 62.69 in control samples (C-FP). OH-OD-treated samples exhibited the least weight loss (6.73%) versus 17.75% in C-FP. Microbial analysis showed that E-FP samples maintained the lowest total viable count by day 8 (3.98 ± 0.02 log CFU/g), compared to OH-OD-FP (4.43 ± 0.13 log CFU/g) and C-FP (4.79 ± 0.06 log CFU/g), confirming the antimicrobial efficacy of the edible coating enriched with rosemary essential oil and ascorbic acid. Sensory evaluation further confirmed that coated samples retained superior sensory qualities, receiving the highest overall acceptance score of 8.86 ± 0.80, compared to values of 7.80 ± 0.98 for control samples (C-FP) and 2.80 ± 0.69 for OH-OD-FP samples, highlighting their enhanced consumer appeal. These findings highlight that combining advanced preservation techniques with MAP can significantly reduce moisture loss and microbial spoilage while maintaining freshness and sensory appeal. This integrated approach offers a promising solution for extending shelf life, reducing food waste, and supporting sustainability in response to consumer demand for minimally processed, high-quality fresh products. Full article

The rapid expansion of urban rail networks has resulted in increasingly complex passenger flow patterns, presenting significant challenges for operational management, especially during incidents and emergencies. Disruptions such as power equipment failures, trackside faults, and train malfunctions can severely impact transit efficiency and reliability, leading to congestion and cascading network effects. Existing models for predicting passenger origin–destination (OD) matrices struggle to provide accurate and timely predictions under these disrupted conditions. This study proposes a deep counterfactual inference model that improves both the prediction accuracy and interpretability of OD matrices during incidents. The model uses a dual-channel framework based on multi-task learning, where the factual channel predicts OD matrices under normal conditions and the counterfactual channel estimates OD matrices during incidents, enabling the quantification of the spatiotemporal impacts of disruptions. Our approach which incorporates KL divergence-based propensity matching enhances prediction accuracy by 4.761% to 12.982% compared to baseline models, while also providing interpretable insights into disruption mechanisms. The model reveals that incident types vary in delay magnitude, with power equipment incidents causing the largest delays, and shows that incidents have time-lag effects on OD flows, with immediate impacts on origin stations and progressively delayed effects on destination and neighboring stations. This research offers practical tools for urban rail transit operators to estimate incident-affected passenger volumes and implement more efficient emergency response strategies, advancing emergency response capabilities in smart transit systems. Full article

Zeroing Neural Networks (ZNNs), an ODE-based neural dynamics framework, has become a pivotal approach for solving time-varying problems in dynamic systems. This paper systematically reviews recent advances in improving the convergence of ZNN models, focusing on the optimization of fixed parameters, dynamic parameters, and activation functions. Additionally, structural adaptations and fuzzy control strategies have significantly enhanced the robustness and disturbance rejection capabilities of these systems. ZNNs have been successfully applied in robotic control, demonstrating superior accuracy and robustness compared to traditional methods. Future research directions include exploring nonlinear activation functions, multimodal adaptation strategies, and scalability in real-world environments. Full article

Power line detection (PLD) is a crucial task in the electric power industry where accurate PLD forms the foundation for achieving automated inspections. However, recent top-performing power line detection methods tend to generate thick and noisy edge lines, adding to the difficulties of subsequent tasks. In this work, we propose a multi-scale feature-based PLD method named LUM-Net to allow for the detection of power lines in a crisp and precise way. The algorithm utilizes EfficientNetV1 as the backbone network, ensuring effective feature extraction across various scales. We developed a Coordinated Convolutional Block Attention Module (CoCBAM) to focus on critical features by emphasizing both channel-wise and spatial information, thereby refining the power lines and reducing noise. Furthermore, we constructed the Bi-Large Kernel Convolutional Block (BiLKB) as the decoder, leveraging large kernel convolutions and spatial selection mechanisms to capture more contextual information, supplemented by auxiliary small kernels to refine the extracted feature information. By integrating these advanced components into a top-down dense connection mechanism, our method achieves effective, multi-scale information interaction, significantly improving the overall performance. The experimental results show that our method can predict crisp power line maps and achieve state-of-the-art performance on the PLDU dataset (ODS = 0.969) and PLDM dataset (ODS = 0.943). Full article

Alpine plants face intense ultraviolet (UV) radiation in high-altitude ecosystems, necessitating adaptive mechanisms like tyrosinase-mediated phenolic metabolism for UV protection. This study aimed to characterize the phenolic profile of Corallodiscus flabellatus (or C. flabellata) and elucidate its mechanistic interactions with tyrosinase under high-altitude environments with intense ultraviolet (UV) radiation. Two novel phenylethanoid glycosides (PhGs) and seven known compounds were isolated using silica gel, ODS, and preparative HPLC, with structures determined via NMR, HR-ESI-MS, and acid hydrolysis. Tyrosinase (EC 1.14.18.1) inhibition assays revealed divergent effects: compound 7 (containing a caffeoyl moiety) exhibited potent inhibition (IC₅₀ = 0.23 μM), comparable to arbutin, while other PhGs displayed activation or biphasic responses. Molecular docking analysis demonstrated that compound 7 stabilized tyrosinase via π-π stacking with Phe264 and Cu²⁺ coordination, whereas activating compounds likely acted as substrates. These findings elucidate the dual regulatory function of PhGs, which activate tyrosinase to counteract acute ultraviolet-induced stress and inhibit its activity to attenuate oxidative overload, thereby advancing our understanding of alpine plant adaptation mechanisms. Full article

Physics-Informed Neural Networks (PINNs) have emerged as a powerful approach for integrating physical laws into a deep learning framework, offering enhanced capabilities for solving complex problems. Despite their potential, the practical implementation of PINNs presents significant challenges. This paper explores the application of PINNs to systems of ordinary differential equations (ODEs), focusing on two key challenges: the forward problem of solution approximation and the inverse problem of parameter estimation. We present three detailed case studies involving dynamical systems for tumor growth, gene expression, and the SIR (Susceptible, Infected, Recovered) model for disease spread. This paper outlines the core principles of PINNs and their integration with physical laws during neural network training. It details the steps involved in implementing PINNs, emphasizing the critical role of network architecture and hyperparameter tuning in achieving optimal performance. Additionally, we provide a Python package, ODE-PINN, to reproduce results for ODE-based systems. Our findings demonstrate that PINNs can yield accurate and consistent solutions, but their performance is highly sensitive to network architecture and hyperparameters tuning. These results underscore the need for customized configurations and robust optimization strategies. Overall, this study confirms the significant potential of PINNs to advance the understanding of dynamical systems in biology and epidemiology. Full article