Search Results (15,101)

We establish a relationship between stochastic differential games (SDGs) and a unified forward–backward coupled stochastic partial differential equation (SPDE) with discontinuous Lévy Jumps. The SDGs have q players and are driven by a general-dimensional vector Lévy process. By establishing a vector-form Ito-Ventzell formula and a 4-tuple vector-field solution to the unified SPDE, we obtain a Pareto optimal Nash equilibrium policy process or a saddle point policy process to the SDG in a non-zero-sum or zero-sum sense. The unified SPDE is in both a general-dimensional vector form and forward–backward coupling manner. The partial differential operators in its drift, diffusion, and jump coefficients are in time-variable and position parameters over a domain. Since the unified SPDE is of general nonlinearity and a general high order, we extend our recent study from the existing Brownian motion (BM)-driven backward case to a general Lévy-driven forward–backward coupled case. In doing so, we construct a new topological space to support the proof of the existence and uniqueness of an adapted solution of the unified SPDE, which is in a 4-tuple strong sense. The construction of the topological space is through constructing a set of topological spaces associated with a set of exponents $\{{\gamma }_1,{\gamma }_2,\dots \}$ under a set of general localized conditions, which is significantly different from the construction of the single exponent case. Furthermore, due to the coupling from the forward SPDE and the involvement of the discontinuous Lévy jumps, our study is also significantly different from the BM-driven backward case. The coupling between forward and backward SPDEs essentially corresponds to the interaction between noise encoding and noise decoding in the current hot diffusion transformer model for generative AI. Full article

A high-precision ground microgravity simulation environment serves as the prerequisite and key to studying landing dynamics in microgravity environments. However, the microgravity level accuracy in traditional ground simulation tests is difficult to guarantee and fails to precisely depict the collision behavior of massive spacecraft. To solve such problems, this paper takes the microgravity simulation system based on quasi-zero stiffness (QZS) mechanism as the research object, and simulates a high-precision and high-level microgravity environment. Then, the collision contact force model of the planar foot and high elastic body rubber is established, the landing dynamics research under different microgravity environments is carried out, the influence of different microgravity environments on the landing behavior of large mass spacecraft is analyzed in depth, and ground microgravity simulation experiments are carried out. The results show that the microgravity simulation level reaches 10⁻⁴ g, the error of gravity compensation for each working condition is not more than 4.22%, and the error of sinking amount is not more than 4.61%, which verifies the superior compensation performance of the QZS mechanism and the accuracy of the dynamic model. Full article

The application of nanomaterials as modifiers in the field of asphalt is increasingly widespread, and this paper aims to systematically review research on the impact of nanomaterials on asphalt viscosity. The results find that nanomaterials tend to increase asphalt’s viscosity, enhancing its resistance to high-temperature rutting and low-temperature cracking. Zero-dimension nanomaterials firmly adhere to the asphalt surface, augmenting non-bonding interactions through van der Waals forces and engaging in chemical reactions to form a spatial network structure. One-dimensional nanomaterials interact with non-polar asphalt molecules, forming bonds between tube walls, thereby enhancing adhesion, stability, and resistance to cyclic loading. Meanwhile, these bundled materials act as reinforcement to transmit stress, preventing or delaying crack propagation. Two-dimensional nanomaterials, such as graphene and graphene oxide, participate in chemical interactions, forming hydrogen bonds and aromatic deposits with asphalt molecules, affecting asphalt’s surface roughness and aggregate movement, which exhibit strong adsorption capacity and increase the viscosity of asphalt. Polymers reduce thermal movement and compact asphalt structures, absorbing light components and promoting the formation of a cross-linked network, thus enhancing high-temperature deformation resistance. However, challenges such as poor compatibility and dispersion, high production costs, and environmental and health concerns currently hinder the widespread application of nanomaterial-modified asphalt. Consequently, addressing these issues through comprehensive economic and ecological evaluations is crucial before large-scale practical implementation. Full article

Leveraging its high accuracy and stability, deep-learning-based coronary artery detection technology has been extensively utilized in diagnosing coronary artery diseases. However, traditional algorithms for localizing coronary stenosis often fall short when detecting stenosis in branch vessels, which can pose significant health risks due to factors like imaging angles and uneven contrast agent distribution. To tackle these challenges, we propose a preprocessing method that integrates Hessian-based vascular enhancement and image fusion as prerequisites for deep learning. This approach enhances fuzzy features in coronary angiography images, thereby increasing the neural network’s sensitivity to stenosis characteristics. We assessed the effectiveness of this method using the latest deep learning networks, such as YOLOv10, YOLOv9, and RT-DETR, across various evaluation metrics. Our results show that our method improves ${\mathrm{AP}}_{50}$ accuracy by 4.84% and 5.07% on RT-DETR R101 and YOLOv10-X, respectively, compared to images without special pre-processing. Furthermore, our analysis of different imaging angles on stenosis localization detection indicates that the left coronary artery zero is the most suitable for detecting stenosis with a ${\mathrm{AP}}_{50}(\%)$ value of 90.5. The experimental results have revealed that the proposed method is effective as a preprocessing technique for deep-learning-based coronary angiography image processing and enhances the model’s ability to identify stenosis in small blood vessels. Full article

Scientific evaluation of cultivated land quality (CLQ) is necessary for promoting rational utilization of cultivated land and achieving one of the Sustainable Development Goals (SDGs): Zero Hunger. However, the CLQ evaluation system proposed in previous studies was diversified, and the methods were inefficient. In this study, based on China’s first national standard “Cultivated Land Quality Grade” (GB/T 33469-2016), we constructed a unified county-level CLQ evaluation system by selecting 15 indicators from five aspects—site condition, environmental condition, physicochemical property, nutrient status and field management—and used the Delphi method to calculate the membership degree of the indicators. Taking Jimo district of Shandong Province, China, as a case study, we compared the performance of three machine learning models, including random forest, AdaBoost, and support vector regression, to evaluate CLQ using multi-temporal remote sensing data. The comprehensive index method was used to reveal the spatial distribution of CLQ. The results showed that the CLQ evaluation based on multi-temporal remote sensing data and machine learning model was efficient and reliable, and the evaluation results had a significant positive correlation with crop yield (r was 0.44, p < 0.001). The proportions of cultivated land of high-, medium- and poor-quality were 27.43%, 59.37% and 13.20%, respectively. The CLQ in the western part of the study area was better, while it was worse in the eastern and central parts. The main limiting factors include irrigation capacity and texture configuration. Accordingly, a series of targeted measures and policies were suggested, such as strengthening the construction of farmland water conservancy facilities, deep tillage of soil and continuing to construct well-facilitated farmland. This study proposed a fast and reliable method for evaluating CLQ, and the results are helpful to promote the protection of cultivated land and ensure food security. Full article

The adsorption capacities of ion exchangers with the primary amine (Lewatit^® VP OC 1065) and polyamine (Diaion™ CR20) functional groups relative to Pd(II) and Cu(II) ions were tested in a batch system, taking into account the influence of the acid concentration (HCl: 0.1–6 mol/L; HCl-HNO₃: 0.9–0.1 mol/L HCl—0.1–0.9 mol/L HNO₃), phase contact time (1–240 min), initial concentration (10–1000 mg/L), agitation speed (120–180 rpm), bead size (0.385–1.2 mm), and temperature (293–333 K), as well as in a column system where the variable operating parameters were HCl and HNO₃ concentrations. There were used the pseudo-first order, pseudo-second order, and intraparticle diffusion models to describe the kinetic studies and the Langmuir and Freundlich isotherm models to describe the equilibrium data to obtain better knowledge about the adsorption mechanism. The physicochemical properties of the ion exchangers were characterized by the nitrogen adsorption/desorption analyses, CHNS analysis, Fourier transform infrared spectroscopy, the sieve analysis, and points of zero charge measurements. As it was found, Lewatit^® VP OC 1065 exhibited a better ability to remove Pd(II) than Diaion™ CR20, and the adsorption ability series for heavy metals was as follows: Pd(II) >> Zn(II) ≈ Ni(II) >> Cu(II). The optimal experimental conditions for Pd(II) sorption were 0.1 mol/L HCl, agitation speed 180 rpm, temperature 293 K, and bead size fraction 0.43 mm ≤ f3 < 0.6 mm for Diaion™ CR20 and 0.315–1.25 mm for Lewatit^® VP OC 1065. The maximum adsorption capacities were 289.68 mg/g for Lewatit^® VP OC 1065 and 208.20 mg/g for Diaion™ CR20. The greatest adsorption ability of Lewatit^® VP OC 1065 for Pd(II) was also demonstrated in the column studies. The working ion exchange in the 0.1 mol/L HCl system was 0.1050 g/mL, much higher compared to Diaion™ CR20 (0.0545 g/mL). The best desorption yields of %D₁ = 23.77% for Diaion™ CR20 and 33.57% for Lewatit^® VP OC 1065 were obtained using the 2 mol/L NH₃·H₂O solution. Full article

Electric vehicle (EV) fast charging stations (FCSs) are essential for achieving net-zero carbon emissions. However, their high power demands pose technical hurdles for medium-voltage (MV) distribution networks, resulting in energy losses, equipment performance issues, overheating, and unexpected tripping. Integrating FCSs into the grid requires considering annual and seasonal variations in EV fast-charging energy consumption. Neglecting these variations can lead to either underestimating or overestimating the impacts of FCSs on the networks. This paper introduces a probabilistic method to assess voltage profile violations, overload capacity, and increased power losses due to FCSs. By incorporating annual and seasonal time-series data, the method accounts for uncertainties related to EV fast charging. Applied to an MV feeder in Brazil, our evaluations highlight the impact of annual power consumption seasonality on EV-grid integration studies. Considering seasonal dependency is crucial for precise impact assessments of MV distribution networks. The proposed method aids utility engineers and planners in quantifying and mitigating the effects of EV fast charging, contributing to more reliable MV grid integration strategies. Full article

This review examines the technological bottlenecks, potential solutions, and future development directions in the treatment and resource utilization of semi-coking wastewater (SCOW) in China. By comprehensively investigating the semi-coking industry and analyzing wastewater treatment research hotspots and existing projects, this study systematically explores the current status and challenges of each treatment unit, emphasizing the necessity for innovative wastewater treatment technologies that offer high efficiency, engineering feasibility, environmental friendliness, and effective resource recovery. This review highlights prospects and recommendations, including the development of novel extractants for phenol and ammonia recovery, a deeper understanding of biological enhancement mechanisms, exogenous bio-enhancement materials, and the creation of cost-effective advanced oxidation process (AOP)-based combined processes. Additionally, it underscores the potential for repurposing SCOW as a valuable resource through appropriate treatment, whether recycling for production or other applications. Full article

Video summarization aims to select the most informative subset of frames in a video to facilitate efficient video browsing. Past efforts have invariantly involved training summarization models with annotated summaries or heuristic objectives. In this work, we reveal that features pre-trained on image-level tasks contain rich semantic information that can be readily leveraged to quantify frame-level importance for zero-shot video summarization. Leveraging pre-trained features and contrastive learning, we propose three metrics featuring a desirable keyframe: local dissimilarity, global consistency, and uniqueness. We show that the metrics can well-capture the diversity and representativeness of frames commonly used for the unsupervised generation of video summaries, demonstrating competitive or better performance compared to past methods when no training is needed. We further propose a contrastive learning-based pre-training strategy on unlabeled videos to enhance the quality of the proposed metrics and, thus, improve the evaluated performance on the public benchmarks TVSum and SumMe. Full article

The promising possibility of an organic photodetector (OPD) is emerging in the field of sensing applications for its tunable absorption range, flexibility, and large-scale fabrication abilities. In this work, we fabricated a bulk heterojunction OPD with a device structure of glass/ITO/PEDOT:PSS/P3HT:PC₆₁BM/Al using the spin-coating process and characterized the dark and photocurrent densities at different applied bias conditions for red, green, and blue incident LEDs. The OPD photocurrent density exhibited a magnitude up to 2.5–3 orders higher compared to the dark current density at a −1 V bias while it increased by up to 3–4 orders at zero bias conditions for red, green, and blue lights, showing an increasing trend when a higher voltage is applied in the negative direction. Different OPD inner periphery shapes, the OPD to LED distance, and OPD area were also considered to bring the variation in the OPD dark and photocurrent densities, which can affect the on/off ratio of the OPD–LED hybrid system and is a critical phenomenon for any sensing application. Full article

Integrating coastal topographic and bathymetric data for creating regional seamless topobathymetric digital elevation models of the land/water interface presents a complex challenge due to the spatial and temporal gaps in data acquisitions. The Coastal National Elevation Database (CoNED) Applications Project develops topographic (land elevation) and bathymetric (water depth) regional scale digital elevation models by integrating multiple sourced disparate topographic and bathymetric data models. These integrated regional models are broadly used in coastal and climate science applications, such as sediment transport, storm impact, and sea-level rise modeling. However, CoNED’s current integration method does not address the occurrence of measurable vertical discrepancies between adjacent near-shore topographic and bathymetric data sources, which often create artificial barriers and sinks along their intersections. To tackle this issue, the CoNED project has developed an additional step in its integration process that collectively assesses the input data to define how to transition between these disparate datasets. This new step defines two zones: a micro blending zone for near-shore transitions and a macro blending zone for the transition between high-resolution (3 m or less) to moderate-resolution (between 3 m and 10 m) bathymetric datasets. These zones and input data sources are reduced to a multidimensional array of zeros and ones. This array is compiled into a 16-bit integer representing a vertical assessment for each pixel. This assessed value provides the means for dynamic pixel-level blending between disparate datasets by leveraging the 16-bit binary notation. Sample site RMSE assessments demonstrate improved accuracy, with values decreasing from 0.203–0.241 using the previous method to 0.126–0.147 using the new method. This paper introduces CoNED’s unique approach of using binary code to improve the integration of coastal topobathymetric data. Full article

The image integrity of real-time monitoring is crucial for monitoring crop growth, helping farmers and researchers improve production efficiency and crop yields. Unfortunately, existing schemes just focus on ground equipment and drone imaging, neglecting satellite networks in remote or extreme environments. Given that satellite internet features wide area coverage, we propose SEAIS, a secure and efficient agricultural image storage scheme combining blockchain and satellite networks. SEAIS presents the mathematical model of image processing and transmission based on satellite networks. Moreover, to ensure the integrity and authenticity of image data during pre-processing such as denoising and enhancement, SEAIS includes a secure agricultural image storage and verification method based on blockchain, homomorphic encryption, and zero-knowledge proof. Specifically, images are stored via IPFS, with hash values and metadata recorded on the blockchain, ensuring immutability and transparency. The simulation results show that SEAIS exhibits more stable and efficient processing times in extreme environments. Also, it maintains low on-chain storage overhead, enhancing scalability. Full article

Harnessing the power of Large Language Models (LLMs), this study explores the use of three state-of-the-art LLMs, specifically GPT-3.5-turbo, LLaMA3-8B, and LLaMA3-70B, for crash severity analysis and inference, framing it as a classification task. We generate textual narratives from original traffic crash tabular data using a pre-built template infused with domain knowledge. Additionally, we incorporated Chain-of-Thought (CoT) reasoning to guide the LLMs in analyzing the crash causes and then inferring the severity. This study also examine the impact of prompt engineering specifically designed for crash severity inference. The LLMs were tasked with crash severity inference to: (1) evaluate the models’ capabilities in crash severity analysis, (2) assess the effectiveness of CoT and domain-informed prompt engineering, and (3) examine the reasoning abilities with the CoT framework. Our results showed that LLaMA3-70B consistently outperformed the other models, particularly in zero-shot settings. The CoT and Prompt Engineering techniques significantly enhanced performance, improving logical reasoning and addressing alignment issues. Notably, the CoT offers valuable insights into LLMs’ reasoning process, unleashing their capacity to consider diverse factors such as environmental conditions, driver behavior, and vehicle characteristics in severity analysis and inference. Full article

The International Maritime Organization aims for net-zero carbon emissions in the maritime industry by 2050. Among various alternatives, route optimization holds an important place as it does not require any additional component-related costs. Especially for wind-assisted ships, the effectiveness of different sailing systems can be improved significantly through route optimization. However, finding the ship’s optimal route is computationally expensive when the totality of possible weather conditions is taken into consideration. To determine the optimal route that minimizes energy consumption, an energy model based on the environmental conditions, ship route and ship speed was built using artificial neural networks. The energy consumed for given input data was calculated using a ship dynamics model and a database was generated to train the artificial neural networks, which predict how much energy is consumed depending on the route followed in given environmental conditions. Then, such networks were exploited to derive the optimal routes for all the relevant operational conditions. It was found that route optimization can reduce the overall ship energy consumption depending on the weather conditions of the environment by up to 9.7% without any increase in voyage time and by up to 35% with a 10% delay in voyage time. The proposed methodology can be applied to any ship by training real weather conditions and provides a framework for reducing energy consumption and greenhouse gas emissions during the service life of ships. Full article

Bulk high-temperature superconductors (HTSs) such as REBa₂Cu₃O_7−x (REBCO, RE = Y, Gd) are commonly used in rotationally symmetric superconducting magnetic bearings. However, such bulks have several disadvantages such as brittleness, limited availability and high costs due to the time-consuming and energy-intensive fabrication process. Alternatively, tape stacks of HTS-coated conductors might be used for these devices promising an improved bearing efficiency due to a simplification of manufacturing processes for the required shapes, higher mechanical strength, improved thermal performance, higher availability and therefore potentially reduced costs. Hence, tape stacks with a base area of (12 × 12) mm² and a height of up to 12 mm were prepared and compared to commercial bulks of the same size. The trapped field measurements at 77 K showed slightly higher values for the tape stacks if compared to bulks with the same size. Afterwards, the maximum levitation forces in zero field (ZFC) and field cooling (FC) modes were measured while approaching a permanent magnet, which allows the stiffness in the vertical and lateral directions to be determined. Similar levitation forces were measured in the vertical direction for bulk samples and tape stacks in ZFC and FC modes, whereas the lateral forces were almost zero for stacks with the REBCO films parallel to the magnet. A 90° rotation of the tape stacks with respect to the magnet results in the opposite behavior, i.e., a high lateral but negligible vertical stiffness. This anisotropy originates from the arrangement of decoupled superconducting layers in the tape stacks. Therefore, a combination of stacks with vertical and lateral alignment is required for stable levitation in a bearing. Full article