<p>Clouds play a crucial role in the Earth's energy budget and the hydrological cycle. However, differences in the spatiotemporal resolution of satellite sensors and in retrieval algorithms lead to substantial heterogeneity among retrieved cloud products. Based on global geostationary satellite thermal infrared brightness temperature data from the Gridded Satellite (GridSat-B1) project, this study applied the single-layer Cloud retrieval model – Small Attention-UNet (Cloud-SmaAtUNet) algorithm in the DaYu CLoud Analysis System (DaYu-CLAS) to retrieve a global cloud product with a 3 <span class="inline-formula">h</span> temporal resolution, 0.07° spatial resolution, and 23 year temporal span (2000–2022). This product is referred to as the DaYu Global Cloud physical properties Products (DaYu-GCP). The DaYu-GCP includes CLoud Phase (CLP), Cloud Top Height (CTH), Cloud Optical Thickness (COT), and Cloud Effective Radius (CER), covering all regions between 70° S–70° N and 180° W–180° E. Evaluation based on the Moderate-resolution Imaging Spectroradiometer (MODIS) official cloud products shows that the annual CLP identification accuracy of DaYu-GCP remains stable at 85 % <span class="inline-formula">±</span> 0.7 %, while the annual RMSE for CTH, COT, and CER stabilize at 1.50 <span class="inline-formula">±</span> 0.03 <span class="inline-formula">km</span>, 10.71 <span class="inline-formula">±</span> 0.15, and 6.75 <span class="inline-formula">±</span> 0.10 <span class="inline-formula">µm</span>, respectively. The multi-year variations in accuracy are within 2 %, with no evident interannual differences, and the spatiotemporal distributions are continuous. In addition, evaluation based on observations from the Cloud Profiling Radar and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) indicates that the DaYu-GCP products show reasonable day–night consistency for optically thin cloud. Furthermore, the DaYu-GCP products are compared with other global cloud products. Taking the Northern Hemisphere as an example, the interannual variations of Cloud Cover Frequency (CCF), CTH, COT, and CER retrieved from DaYu-GCP show correlation coefficients of 0.760, 0.486, 0.764, and 0.514 with the ISCCP product, respectively, and 0.444, 0.778, 0.171, and 0.412 with the CLARA-A3 product. The DaYu-GCP dataset, which is stored in the Network Common Data Format (NetCDF), is freely available on the Science Data Bank at <a href="https://doi.org/10.57760/sciencedb.26292">https://doi.org/10.57760/sciencedb.26292</a> (Zhao et al., 2026). The corresponding code can be found at <span class="uri">https://github.com/lingxiao-zhao/DaYu-GCP</span> (last access: 25 June 2025).</p>
The dual-pulse heterodyne demodulation distributed acoustic sensing (HD-DAS) system has superior performance but is fundamentally limited by the short sensing range, which poses a significant obstacle to its application in long-distance monitoring. This paper proposes and experimentally demonstrates a novel binary-tree structure DAS (BTS-DAS) aimed at overcoming this critical limitation. By physically decoupling the long-distance transmission fiber from the final sensing part, this structure effectively expands the system’s remote sensing capability without compromising the high pulse repetition rate for high-performance measurement. We identified modulation instability (MI), rather than stimulated Brillouin scattering (SBS), as the dominant nonlinear noise source in the extended fiber chain. Through careful power management, we established an optimal launch power window. The practical feasibility of the system was verified during on-site testing, where vibrations were successfully detected over a 10 km transmission link with sensing occurring in the 250 m sensing fiber segment, achieving a low background noise of −59.79 dB ref rad/<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msqrt><mi>Hz</mi></msqrt></semantics></math></inline-formula>. This work presents a robust and scalable solution for long-range, high-performance acoustic sensing.
Abstract During the metal cutting process, especially in continuous contact conditions like turning, the challenge of lubricants failing to effectively reach the cutting point remains unresolved. Micro-textured cutting tools offer a potential solution for tool-chip contact challenges. Inspired by the evolutionary achievements of the biosphere, micro-textures are expected to overcome lubrication limitations in cutting zones. Drawing on the anti-gravity water transport seen at the mouth edge of the Nepenthes plant, an innovative microchannel with Nepenthes-shaped contours was designed on the rake face to enable controlled lubricant transport. However, the dynamics of lubricant delivery on textured surfaces are not fully understood. This study first analyzed the microstructure and water transport mechanism of Nepenthes to reconstruct a micro-textured surface for controlled lubricant transport. A dynamic model was then developed to describe lubricant transport within open microchannels, with mathematical simulations predicting transport speed and flow distance. To validate this model, diffusion experiments of alumina soybean oil nanolubricant on polycrystalline diamond (PCD) cutting tool surfaces were conducted, showing an average prediction deviation of 5.01%. Compared with the classical Lucas-Washburn model, the new model improved prediction accuracy by 4.72%. Additionally, comparisons were made to examine droplet spreading and non-uniform diffusion on textured surfaces, revealing that the T2 surface exhibited the strongest unidirectional diffusion characteristics. The contact angle ratio, droplet unidirectional spreading ratio, and droplet spreading aspect ratio were 0.48, 1.75, and 3.99, respectively. Finally, the anti-wear, friction-reducing, and efficiency-enhancing mechanisms of micro-textured surfaces in minimum quantity lubrication turning were analyzed. This approach may support continuous cutting of difficult-to-machine materials.
Ocean engineering, Mechanical engineering and machinery
This study investigates the air movement preference of males and females after moderate-intensity exercise. 35 participants dressed in 0.6 clo exercised for 15 min in a room at 30 °C and then entered another room at 24 °C/26 °C/28 °C. During the experiment, participants were able to adjust the fan speed according to their own thermal comfort needs. The results indicate that after a change in metabolic rate, female prefer higher fan usage and greater air movement compared to males. When the body returns to thermal comfort, male have higher fan usage and prefer higher air movement than female. There were no difference in subjective evaluation and skin temperature between female and male. However, the skin evaporative heat loss of female was significantly lower than that of male. The correlation between air temperature, air speed and the time after entering the room tailored to the thermal requirements of distinct genders following moderate-intensity exercise has been established, which can provide a comprehensive control strategy for achieving both comfortable and energy-efficient thermal environments.
Johan Cederbladh, Loek Cleophas, Eduard Kamburjan
et al.
With the current trend in Model-Based Systems Engineering towards Digital Engineering and early Validation & Verification, experiments are increasingly used to estimate system parameters and explore design decisions. Managing such experimental configuration metadata and results is of utmost importance in accelerating overall design effort. In particular, we observe it is important to 'intelligent-ly' reuse experiment-related data to save time and effort by not performing potentially superfluous, time-consuming, and resource-intensive experiments. In this work, we present a framework for managing experiments on digital and/or physical assets with a focus on case-based reasoning with domain knowledge to reuse experimental data efficiently by deciding whether an already-performed experiment (or associated answer) can be reused to answer a new (potentially different) question from the engineer/user without having to set up and perform a new experiment. We provide the general architecture for such an experiment manager and validate our approach using an industrial vehicular energy system-design case study.
This paper introduces Design for Sensing and Digitalisation (DSD), a new engineering design paradigm that integrates sensor technology for digitisation and digitalisation from the earliest stages of the design process. Unlike traditional methodologies that treat sensing as an afterthought, DSD emphasises sensor integration, signal path optimisation, and real-time data utilisation as core design principles. The paper outlines DSD's key principles, discusses its role in enabling digital twin technology, and argues for its importance in modern engineering education. By adopting DSD, engineers can create more intelligent and adaptable systems that leverage real-time data for continuous design iteration, operational optimisation and data-driven predictive maintenance.
Muhammad Tayyab Khan, Zane Yong, Lequn Chen
et al.
Accurate extraction of key information from 2D engineering drawings is crucial for high-precision manufacturing. Manual extraction is slow and labor-intensive, while traditional Optical Character Recognition (OCR) techniques often struggle with complex layouts and overlapping symbols, resulting in unstructured outputs. To address these challenges, this paper proposes a novel hybrid deep learning framework for structured information extraction by integrating an Oriented Bounding Box (OBB) detection model with a transformer-based document parsing model (Donut). An in-house annotated dataset is used to train YOLOv11 for detecting nine key categories: Geometric Dimensioning and Tolerancing (GD&T), General Tolerances, Measures, Materials, Notes, Radii, Surface Roughness, Threads, and Title Blocks. Detected OBBs are cropped into images and labeled to fine-tune Donut for structured JSON output. Fine-tuning strategies include a single model trained across all categories and category-specific models. Results show that the single model consistently outperforms category-specific ones across all evaluation metrics, achieving higher precision (94.77% for GD&T), recall (100% for most categories), and F1 score (97.3%), while reducing hallucinations (5.23%). The proposed framework improves accuracy, reduces manual effort, and supports scalable deployment in precision-driven industries.
In response to the deficiencies in existing bridge pier scour protection technologies, this paper introduces a novel protective device, namely a normal distribution-shaped surface (BND) protection devices formed by rotating a concave normal curve. A three-dimensional turbulent SST <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>k</mi><mo>−</mo><mi>ω</mi></mrow></semantics></math></inline-formula> model is constructed, and physical model experiments of conical surfaces are conducted to validate the mathematical model. The simulation analyzes longitudinal water flow, downflow, vorticity intensity, and shear stress within normal and conical surfaces. The results show that the downflow distribution in front of the pier spans a relative water depth of (−0.45, 0.67), with a peak velocity approximately 70% of the longitudinal flow velocity. Circulation forms within the surfaces, with the main vortex flux inside the BND being 33% lower than that inside the conical surface. The maximum shear stress coefficient inside the BND can reach 9, and the protective surface isolates the bed from the flow to prevent scouring by high shear stress. The velocity gradient at the edge of the surface is small, and the edge shear stress of the 3D normal distribution-shaped surface (BND) protection device is only one-third of that of the conical surface, preventing edge scouring. The large shear stress and its distribution area decrease monotonically with the increase in surface width. When the surface width is four times the diameter, the distribution range of the shear stress coefficient greater than 1 is very small. The study of three-Dimensional turbulence within the BND provides a numerical basis for an anti-scour design.
Iqbal Ifrah, Boulaaras Salah Mahmoud, Rehman Hamood Ur
et al.
The nonlinear Schrödinger equation, held in high regard in the realms of plasma physics, fluid mechanics, and nonlinear optics, reverberates deeply within the field of ocean engineering, imparting profound insights across a plethora of phenomena. This article endeavours to establish a connection between the equation’s theoretical framework and its practical applications in ocean engineering, presenting a range of solutions tailored to grasp the intricacies of water wave propagation. By employing three methodologies, namely, the simplest equation method, the ratio technique, and the modified extended tanh-function method, we delineate various wave typologies, encompassing solitons and periodic manifestations. Enhanced by visual representations, our findings have the potential to deepen the comprehension of wave dynamics, with promising implications for the advancement of ocean engineering technologies and the refinement of marine architectural design.
Aiming at the problems of siltation promotion and siltation return in the beach area of Hangzhou Bay, a numerical calculation model of hydrodynamic and scour and siltation in the south bank of Ningbo Bay was constructed, and the reliability of the short-term scour and siltation numerical calculation model was verified by combining with the field measured scour and siltation data, so as to obtain the natural siltation situation in this area under a longer time scale in the future. Meanwhile, the siltation promotion scheme was selected by comparing with artificial embankment construction. The research results show that the restoration effect of natural silt beach under the action of excavation diversion is remarkable, and the construction of barrier has a significant effect on the retention and sedimentation of sediment in the trench. After the construction of the barrier with a height of -2.22m, the average thickness of sediment promotion in the low-lying area reaches 0.22m, and the elevation of seabed in the low-lying area can reach about 2.5m. Therefore, it is particularly effective for the sediment deposition in low-lying areas in the high sediment content sea area to adopt the method of natural diversion and separation embankment.
The Russia–Ukraine conflict has persisted for over a year, posing challenges in assessing and verifying the extent of damage through on-site investigations. Nighttime light (NTL) remote sensing, an emerging approach for studying regional conflicts, can complement traditional methods. This article employs National Aeronautics and Space Administration's Black Marble products to reveal the response characteristics of NTL intensity at national and state scales during the first anniversary of the conflict (January 2022 to February 2023) in Ukraine. The article used the NTL ratio index to assess the relative intensity of NTL and month-on-month change rate, nighttime light change rate index (NLCRI), and the rate (<italic>R</italic> value) of linear regression analysis to depict spatiotemporal dynamics. In addition, Theil–Sen median trend analysis and Mann–Kendall tests were employed to analyze intensity trends, with a “dual-threshold method” to reduce extensive noise interference. The results showed: At the national scale, the conflict resulted in an 84.0% decrease in NTL across Ukraine. At the state scale, the most severe NTL decline occurred near the southwestern border and eastern conflict zone under Ukrainian government control, witnessing over 80% decline rates. The correlation of decreases in NLCRI and <italic>R</italic> values with population displacement, infrastructure damage, or curfew measures demonstrated that the concentration of refugees and electricity facility restoration led to increased NLCRI and <italic>R</italic> values. Overall, NTL reflects critical moments at the national scale and provides insights into military intentions and humanitarian measures at the state scale. Therefore, NTL can effectively serve as a tool for observation and assessment in military conflicts.
Abstract The aim of this work is to examine the effects of vitamin E addition to water on the structure of the gill tissue and energy metabolism of crucian carp (Carassius auratus) under cooling stress. The crucian carp were chilled using a cold acclimation intelligent chilling equipment from 20 °C to 5 °C. They were divided into three groups: the control group (E1), the negative control group (E2), and the 100 mg/L vitamin E (E3) solution. Three different temperature points (20 °C, 10 °C, and 5 °C) were used to collect, test, and analyze the samples. The findings demonstrated that in the E3 treatment group, phosphoenolpyruvate carboxykinase, acetyl coenzyme A carboxylase, total cholesterol, urea nitrogen, triglyceride, and fatty acid synthase contents were significantly lower under cooling stress than those in the E1 and E2 treatment groups (P < 0.05). The E3 therapy group had significantly greater blood glucose, glycogen, and glycogen synthase levels than the E1 and E2 treatment groups (P < 0.05). The levels of pyruvate kinase in the E1, E2, and E3 treatment groups did not differ significantly. Crucian carp's gill tissue changed under cooling stress, including capillary dilatation, and the E3 treatment group experienced less damage overall than the E1 and E2 treatment groups. In conclusion, supplementing water with vitamin E to treat crucian carp can decrease damage, improve the body's ability to withstand cold, and slow down the stress response brought on by cooling stress. This provides a theoretical basis for supplementing water with vitamin E to fish stress relief.
This study proposes to automate the analysis of wiring diagrams to generate cable lists by using machine learning for text classification and pre-trained Deep Neural Network (DNN)-based image classification to detect cable routes. In shipyards, many drawings are produced for each ship, and analyzing these drawings, especially wiring diagrams, to generate cable lists for the Bill of Materials (BOM) can be a time-consuming and error-prone task. This process is performed manually by reading the cable routes and entering the information into a spreadsheet. To address these challenges, this study aims to automate the information extraction from wiring diagrams. The process involves extracting text from the PDF document and classifying it using machine learning, followed by using DNN-based image classification to trace cable routes and identify the relevant annotations. The developed algorithm was tested on three PDF wiring diagram samples and achieved an average accuracy of about 90%, confirming its effectiveness.
The purpose of this note is to present an enhancement to a Maxey-Riley theory proposed in recent years for the dynamics of inertial particles on the ocean surface. This model upgrade removes constraints on the reserve buoyancy, defined as the fraction of the particle volume above the ocean surface. The refinement results in an equation that correctly describes both the neutrally buoyant and fully buoyant particle scenarios.
Chenggong Wang, Michael S. Pritchard, Noah Brenowitz
et al.
Seasonal climate forecasts are socioeconomically important for managing the impacts of extreme weather events and for planning in sectors like agriculture and energy. Climate predictability on seasonal timescales is tied to boundary effects of the ocean on the atmosphere and coupled interactions in the ocean-atmosphere system. We present the Ocean-linked-atmosphere (Ola) model, a high-resolution (0.25°) Artificial Intelligence/ Machine Learning (AI/ML) coupled earth-system model which separately models the ocean and atmosphere dynamics using an autoregressive Spherical Fourier Neural Operator architecture, with a view towards enabling fast, accurate, large ensemble forecasts on the seasonal timescale. We find that Ola exhibits learned characteristics of ocean-atmosphere coupled dynamics including tropical oceanic waves with appropriate phase speeds, and an internally generated El Niño/Southern Oscillation (ENSO) having realistic amplitude, geographic structure, and vertical structure within the ocean mixed layer. We present initial evidence of skill in forecasting the ENSO which compares favorably to the SPEAR model of the Geophysical Fluid Dynamics Laboratory.
Global climate change is causing various negative impacts on urban ecosystems and energy systems. To effectively mitigate and adapt to these changes, it is important to understand the contributions of background climate and local effects to urban thermal environment variation. This study utilized the empirical orthogonal function (EOF) approach to deconstruct long-term MODIS land surface temperature (LST) datasets to obtain the main features of change in daytime and nighttime thermal environments. Local bivariate spatial autocorrelation analysis was used to explore the underlying causes of these changes. The main EOF modes explained 73.14% and 81.33% of daytime and nighttime thermal environment variation, respectively. The correlation coefficient between the time coefficient of the main modes and the average LST was > 0.99, reflecting the role of global effect caused by background climate change. The secondary EOF modes explained 12.51% and 4.12% of daytime and nighttime thermal environment variation, respectively, and were spatially correlated with changes in landscape thermal intensity, reflecting local effect caused by landscape change and anthropogenic heat emissions. In expansion and renewal areas, industrial zones and compact high-rise buildings had the most obvious warming effect on the daytime thermal environment, while mid-to-high-rise buildings had the most obvious warming effect on the nighttime thermal environment. The results of this study provide valuable insights into the mechanisms of background climate and local effects on the urban thermal environment, and provide a reference for formulating effective strategies for mitigating and adapting to change in urban areas, and for promoting sustainable development.
Regeneration is a complex process influenced by many independent or combined factors, including inflammation, proliferation, and tissue remodeling. The ocean, the most extensive resource on Earth, is rich in Seaweed. With increasing research in recent years, researchers have discovered that seaweed polysaccharides have various pharmacological effects, including a particular efficacy in promoting bone tissue regeneration. However, the application of this material in the field of bone tissue engineering is very limited. However, there are few studies on the polysaccharide at home and abroad, and little is known about its potential application value in bone repair. In addition, the bioavailability of the seaweed polysaccharide is also low, and there are still many problems to be solved. For example, the ease of solubility of fucoidan in water is a key issue that restricts its practical application. In this review, we summarize the applications and mechanisms of seaweed polysaccharides in bone healing. We also propose to combine seaweed polysaccharides with novel technologies through different types of preparations, hydrogels, scaffolds, and 3D printing to improve their use in tissue healing and regeneration.
Science, General. Including nature conservation, geographical distribution
Point-based and voxel-based methods can learn the local features of point clouds. However, although point-based methods are geometrically precise, the discrete nature of point clouds negatively affects feature learning performance. Moreover, although voxel-based methods can exploit the learning power of convolutional neural networks, their resolution and detail extraction may be inadequate. Therefore, in this study, point-based and voxel-based methods are combined to enhance localization precision and matching distinctiveness. The core procedure is embodied in V2PNet, an innovative fused neural network that we design to perform voxel-to-pixel propagation and fusion, which seamlessly integrates the two encoder–decoder branches. Experiments are conducted on indoor and outdoor benchmark datasets with different platforms and sensors, i.e., the 3DMatch and Karlsruhe Institute of Technology and Toyota Technological Institute datasets, with the registration recall of 89.4% and the success rate of 99.86%, respectively. Qualitative and quantitative evaluations demonstrate that V2PNet has shown improvements in semantic awareness, geometric structure discernment, and other performance metrics.
LIU Lu, HONG Pengzhi, ZHOU Chunxia, SONG Chunyong, ZHANG Ruolan, ZHONG Tanjun
In order to improve the quality of freshwater fish surimi products, the effect of adding 4% of native cassava starch or modified cassava starch (starch phosphate, hydroxypropyl starch, acetylated distarch phosphate, or a 3:2 (m/m) mixture of acetylated distarch phosphate and hydroxypropyl starch) on improving the quality of surimi gels prepared from frozen tilapia surimi was investigated by measuring texture, rheological properties, water-holding capacity, moisture distribution and microstructure. The results showed that compared with the control group, the whiteness of tilapia surimi gels with native and modified cassava starch significantly decreased (P < 0.05), the gel strength, hardness, chewiness and water-holding capacity significantly increased (P < 0.05), the percentage of cooking loss decreased (P < 0.05), the bound water content increased (P < 0.05), and the microstructure was more uniform and compact. The best gel texture and highest water-holding capacity were achieved with the addition of the modified starch mixture. Low-field magnetic resonance imaging and optical microscopy results showed that both native and modified cassava starch could effectively keep the water in the surimi gel. The native and modified starch could absorb water and expand during the heating process, concentrating and filling the surimi gel network structure, and the surimi gel structure with the modified starch mixture was most compact and uniform. Therefore, addition of acetylated distarch phosphate-hydroxypropyl starch mixture can effectively improve the quality of tilapia surimi products, which will provide theoretical support for the development of freshwater fish surimi products.