Search Results (3,154)

Ship collision accidents have a greatly adverse impact on the development of the shipping industry. Due to the uncertainty relating to these accidents, maritime risk is often difficult to accurately quantify. This study innovatively proposes a comprehensive method combining qualitative and quantitative methods to predict the risk of ship collision accidents. First, in view of the uncertain impact of risk factors, the Bayesian network analysis method was used to characterize the correlations between risk factors, and a collision accident risk assessment network model was established. Secondly, in view of the uncertainty relating to the information about risk factors, a subjective data quantification method based on the cloud model was adopted, and the quantitative reasoning of collision accident risk was determined based on multi-source data fusion. The proposed method was applied to the spatiotemporal analysis of ship collision accident risk in China’s coastal port waters. The results show that there is a higher risk of collision accidents in Guangzhou Port and Ningbo Port in China, the potential for ship collision accidents in southern China is greater, and the occurrence of ship collision accidents is most affected by the environment and operations of operators. Combining the Bayesian network and cloud model and integrating multi-source data information to conduct an accident risk assessment, this innovative analysis method has significance for improving the prevention of and response to risks of ship navigation operations in China’s coastal ports. Full article

The present study introduces a comprehensive, open-access atlas of mesoscale eddies in the global ocean, as identified and tracked by the TOEddies algorithm implemented on a global scale. Unlike existing atlases, TOEddies detects eddies directly from absolute dynamic topography (ADT) without spatial filtering, preserving the natural spatial variability and enabling precise, high-resolution tracking of eddy dynamics. This dataset provides daily information on eddy characteristics, such as size, intensity, and polarity, over a 30-year period (1993–2023), capturing complex eddy interactions, including splitting and merging events that often produce networks of interconnected eddies. This unique approach challenges the traditional single-trajectory perspective, offering a nuanced view of eddy life cycles as dynamically linked trajectories. In addition to traditional metrics, TOEddies identifies both the eddy core (characterized by maximum azimuthal velocity) and the outer boundary, offering a detailed representation of eddy structure and enabling precise comparisons with in situ data. To demonstrate its value, we present a statistical overview of eddy characteristics and spatial distributions, including generation, disappearance, and merging/splitting events, alongside a comparative analysis with existing global eddy datasets. Among the multi-year observations, TOEddies captures coherent, long-lived eddies with lifetimes exceeding 1.5 years, while highlighting significant differences in the dynamic properties and spatial patterns across datasets. Furthermore, this study integrates TOEddies with 23 years of colocalized Argo profile data (2000–2023), allowing for a novel examination of eddy-induced subsurface variability and the role of mesoscale eddies in the transport of global ocean heat and biogeochemical properties. This atlas aims to be a valuable resource for the oceanographic community, providing an open dataset that can support diverse applications in ocean dynamics, climate research, and marine resource management. Full article

The vitality of a city is shaped by its social structure, environmental quality, and spatial form, with child-friendliness being an essential component of urban vitality. While there are numerous qualitative studies on the relationship between child-friendliness and various indicators of urban vitality, quantitative research remains relatively scarce, leading to a lack of sufficient objective and trustworthy data to guide urban planning and the development of child-friendly cities. This paper presents an analytical framework, using Heping District in Tianjin, China, as a case study. It defines four main indicators—social vitality, environmental vitality, spatial vitality, and urban scene perception—for a trustworthy and transparent quantitative evaluation. The study integrates multi-source data, including primary education (POI) data, street view image (SVI) data, spatiotemporal big data, normalized difference vegetation index (NDVI), and large visual language models (LVLMs) for the trustworthy analysis. These data are visualized using corresponding big data and weighted analysis methods, ensuring transparent and accurate assessments of the child-friendliness of urban blocks. This research introduces an innovative and trustworthy method for evaluating the child-friendliness of urban blocks, addressing gaps in the quantitative theory of child-friendliness in urban planning. It also provides a practical and reliable tool for urban planners, offering a solid theoretical foundation to create environments that better meet the needs of children in a trustworthy manner. Full article

Humans typically make decisions based on past experiences and observations, while in the field of robotic manipulation, the robot’s action prediction often relies solely on current observations, which tends to make robots overlook environmental changes or become ineffective when current observations are suboptimal. To address this pivotal challenge in robotics, inspired by human cognitive processes, we propose our method which integrates historical learning and multi-view attention to improve the performance of robotic manipulation. Based on a spatio-temporal attention mechanism, our method not only combines observations from current and past steps but also integrates historical actions to better perceive changes in robots’ behaviours and their impacts on the environment. We also employ a mutual information-based multi-view attention module to automatically focus on valuable perspectives, thereby incorporating more effective information for decision-making. Furthermore, inspired by human visual system which processes both global context and local texture details, we have devised a method that merges semantic and texture features, aiding robots in understanding the task and enhancing their capability to handle fine-grained tasks. Extensive experiments in RLBench and real-world scenarios demonstrate that our method effectively handles various tasks and exhibits notable robustness and adaptability. Full article

With a view to improve measurements, this paper presents a statistical approach for characterizing the behaviour of roughness parameters based on measurements performed on ground surface topographies (grit #080/#120). A S neox^TM (Sensofar^®, Terrassa, Spain), equipped with three optical instrument modes (Focus Variation (FV), Coherence Scanning Interferometry (CSI), and Confocal Microscopy (CM)), is used according to a specific measurement plan, called Morphomeca Monitoring, including topography representativeness and several time-based measurements. Previously applied to the Sa parameter, the statistical approach based here solely on the Quality Index (QI) has now been extended to a multi-parameter approach. Firstly, the study focuses on detecting and explaining parameter disturbances in raw data by identifying and quantifying outliers of the parameter’s values, as a new first indicator. This allows us to draw parallels between these outliers and the surface topography, providing reflection tracks. Secondly, the statistical approach is applied to highlight disturbed parameters concerning the instrument mode used and the concerned grit level with two other indicators computed from QI, named homogeneity and number of modes. The applied method shows that a cleaning of the data containing the parameters values is necessary to remove outlier values, and a set of roughness parameters could be determined according to the assessment of the indicators. The final aim is to provide a set of parameters which best describe the measurement conditions based on monitoring data, statistical indexes, and surface topographies. It is shown that the parameters Sal, Sz and Sci are the most reliable roughness parameters, unlike Sdq and S5p, which appear as the most unstable parameters. More globally, the volume roughness parameters appear as the most stable, differing from the form parameters. This investigated point of view offers thus a complementary framework for improving measurement processes. In addition, this method aims to provide a global and more generalizable alternative than traditional methods of uncertainty calculation, based on a thorough analysis of multi-parameter and statistical indexes. Full article

With the current demand for automation in the agro-food industry, accurately detecting and localizing relevant objects in 3D is essential for successful robotic operations. However, this is a challenge due the presence of occlusions. Multi-view perception approaches allow robots to overcome occlusions, but a tracking component is needed to associate the objects detected by the robot over multiple viewpoints. Multi-object tracking (MOT) algorithms can be categorized between two-stage and single-stage methods. Two-stage methods tend to be simpler to adapt and implement to custom applications, while single-stage methods present a more complex end-to-end tracking method that can yield better results in occluded situations at the cost of more training data. The potential advantages of single-stage methods over two-stage methods depend on the complexity of the sequence of viewpoints that a robot needs to process. In this work, we compare a 3D two-stage MOT algorithm, 3D-SORT, against a 3D single-stage MOT algorithm, MOT-DETR, in three different types of sequences with varying levels of complexity. The sequences represent simpler and more complex motions that a robot arm can perform in a tomato greenhouse. Our experiments in a tomato greenhouse show that the single-stage algorithm consistently yields better tracking accuracy, especially in the more challenging sequences where objects are fully occluded or non-visible during several viewpoints. Full article

With the continuous progress of biomedical technology, biomaterial coatings play an important role in improving the performance of medical devices and promoting tissue repair and regeneration. The application of natural medicine to biological materials has become a hot topic due to its diverse biological activity, low toxicity, and wide range of sources. This article introduces the definition and classification of natural medicines, lists some common natural medicines, such as curcumin, allicin, chitosan, tea polyphenols, etc., and lists some biological activities of some common natural medicines, such as antibacterial, antioxidant, antitumor, and other properties. According to the different characteristics of natural medicines, physical adsorption, chemical grafting, layer-by-layer self-assembly, sol–gel and other methods are combined with biomaterials, which can be used for orthopedic implants, cardiovascular and cerebrovascular stents, wound dressings, drug delivery systems, etc., to exert their biological activity. For example, improving antibacterial properties, promoting tissue regeneration, and improving biocompatibility promote the development of medical health. Although the development of biomaterials has been greatly expanded, it still faces some major challenges, such as whether the combination between the coating and the substrate is firm, whether the drug load is released sustainably, whether the dynamic balance will be disrupted, and so on; a series of problems affects the application of natural drugs in biomaterial coatings. In view of these problems, this paper summarizes some suggestions by evaluating the literature, such as optimizing the binding method and release system; carrying out more clinical application research; carrying out multidisciplinary cooperation; broadening the application of natural medicine in biomaterial coatings; and developing safer, more effective and multi-functional natural medicine coatings through continuous research and innovation, so as to contribute to the development of the biomedical field. Full article

In view of the issues of missed and false detections encountered in small object detection for UAV remote sensing images, and the inadequacy of existing algorithms in terms of complexity and generalization ability, we propose a small object detection model named IA-YOLOv8 in this paper. This model integrates the intra-group multi-scale fusion attention mechanism and the adaptive weighted feature fusion approach. In the feature extraction phase, the model employs a hybrid pooling strategy that combines Avg and Max pooling to replace the single Max pooling operation used in the original SPPF framework. Such modifications enhance the model’s ability to capture the minute features of small objects. In addition, an adaptive feature fusion module is introduced, which is capable of automatically adjusting the weights based on the significance and contribution of features at different scales to improve the detection sensitivity for small objects. Simultaneously, a lightweight intra-group multi-scale fusion attention module is implemented, which aims to effectively mitigate background interference and enhance the saliency of small objects. Experimental results indicate that the proposed IA-YOLOv8 model has a parameter quantity of 10.9 MB, attaining an average precision (mAP) value of 42.1% on the Visdrone2019 test set, an mAP value of 82.3% on the DIOR test set, and an mAP value of 39.8% on the AI-TOD test set. All these results outperform the existing detection algorithms, demonstrating the superior performance of the IA-YOLOv8 model in the task of small object detection for UAV remote sensing. Full article

Maize (Zea mays L.) is highly sensitive to temperature during its growth and development stage. A 1 °C drop in temperature can delay maturity by 10 days, resulting in a yield reduction of over 10%. Low-temperature tolerance in maize is a complex quantitative trait, and different germplasms exhibit significant differences in their responses to low-temperature stress. To explore the differences in gene expression and metabolites between B144 (tolerant) and Q319 (susceptible) during germination under low-temperature stress and to identify key genes and metabolites that respond to this stress, high-throughput transcriptome sequencing was performed on the leaves of B144 and Q319 subjected to low-temperature stress for 24 h and their respective controls using Illumina HiSeq^TM 4000 high-throughput sequencing technology. Additionally, high-throughput metabolite sequencing was conducted on the samples using widely targeted metabolome sequencing technology. The results indicated that low-temperature stress triggered the accumulation of stress-related metabolites such as amino acids and their derivatives, lipids, phenolic acids, organic acids, flavonoids, lignin, coumarins, and alkaloids, suggesting their significant roles in the response to low temperature. This stress also promoted gene expression and metabolite accumulation involved in the flavonoid biosynthesis pathway. Notably, there were marked differences in gene expression and metabolites related to the glyoxylate and dicarboxylate metabolism pathways between B144 and Q319. This study, through multi-omics integrated analysis, provides valuable insights into the identification of metabolites, elucidation of metabolic pathways, and the biochemical and genetic basis of plant responses to stress, particularly under low-temperature conditions. Full article

Multicolor emission and dynamic color tuning with large spectral range are challenging to realize but critically important in many areas of technology and daily life, such as general lighting, display, multicolor detection and multi-band communication. Herein, we report an excitation-power-dependent color-tuning emission from an individual Sn-doped CdS nanowire with a large spectral range and continuous color tuning. Its photoluminescence (PL) spectrum shows a broad trap-state emission band out of Sn dopants, which is superposed by whispering-gallery (WG) microcavity due to the nanostructure size and its structure, besides the CdS band-edge emission. By simply changing the excitation power from 0.25 to 1.36 mW, we demonstrate that the typical Sn-doped CdS nanowire with the weight ratio of 10:1 of CdS and SnO₂, the emission color can change from red to orange to yellow to green. In view of the stable properties and large spectral range, the Sn-doped CdS nanowires are very promising potential candidates in nanoscale optoelectronic devices. Full article

Reliable environmental perception capabilities are a prerequisite for achieving autonomous driving. Cameras and LiDAR are sensitive to illumination and weather conditions, while millimeter-wave radar avoids these issues. Existing models rely heavily on image-based approaches, which may not be able to fully characterize radar sensor data or efficiently further utilize them for perception tasks. This paper rethinks the approach to modeling radar signals and proposes a novel U-shaped multilayer perceptron network (U-MLPNet) that aims to enhance the learning of omni-dimensional spatio-temporal dependencies. Our method involves innovative signal processing techniques, including a 3D CNN for spatio-temporal feature extraction and an encoder–decoder framework with cross-shaped receptive fields specifically designed to capture the sparse and non-uniform characteristics of radar signals. We conducted extensive experiments using a diverse dataset of urban driving scenarios to characterize the sensor’s performance in multi-view semantic segmentation and object detection tasks. Experiments showed that U-MLPNet achieves competitive performance against state-of-the-art (SOTA) methods, improving the mAP by 3.0% and mDice by 2.7% in RD segmentation and AR and AP by 1.77% and 2.03%, respectively, in object detection. These improvements signify an advancement in radar-based perception for autonomous vehicles, potentially enhancing their reliability and safety across diverse driving conditions. Full article

Acute myeloid leukemia (AML) is a complex clonal disorder characterized by clinical, genetic, metabolomic, and epigenetic heterogeneity resulting in the uncontrolled proliferation of aberrant blood-forming precursor cells. Despite advancements in the understanding of the genetic, metabolic, and epigenetic landscape of AML, it remains a significant therapeutic challenge. Functional profiling techniques, such as BH3 profiling (BP), gene expression profiling (GEP), proteomics, metabolomics, drug sensitivity/resistance testing (DSRT), CRISPR/Cas9, and RNAi screens offer valuable insights into the functional behavior of leukemia cells. BP evaluates the mitochondrial response to pro-apoptotic BH3 peptides, determining a cell’s apoptotic threshold and its reliance on specific anti-apoptotic proteins. This knowledge can pinpoint vulnerabilities in the mitochondria-mediated apoptotic pathway in leukemia cells, potentially informing treatment strategies and predicting therapeutic responses. GEP, particularly RNA sequencing, evaluates the transcriptomic landscape and identifies gene expression alterations specific to AML subtypes. Proteomics and metabolomics, utilizing mass spectrometry and nuclear magnetic resonance (NMR), provide a detailed view of the active proteins and metabolic pathways in leukemia cells. DSRT involves exposing leukemia cells to a panel of chemotherapeutic and targeted agents to assess their sensitivity or resistance profiles and potentially guide personalized treatment strategies. CRISPR/Cas9 and RNAi screens enable systematic disruption of genes to ascertain their roles in leukemia cell survival and proliferation. These techniques facilitate precise disease subtyping, uncover novel biomarkers and therapeutic targets, and provide a deeper understanding of drug-resistance mechanisms. Recent studies utilizing functional profiling have identified specific mutations and gene signatures associated with aggressive AML subtypes, aberrant signaling pathways, and potential opportunities for drug repurposing. The integration of multi-omics approaches, advances in single-cell sequencing, and artificial intelligence is expected to refine the precision of functional profiling and ultimately improve patient outcomes in AML. This review highlights the diverse landscape of functional profiling methods and emphasizes their respective advantages and limitations. It highlights select successes in how these methods have further advanced our understanding of AML biology, identifies druggable targets that have improved outcomes, delineates challenges associated with these techniques, and provides a prospective view of the future where these techniques are likely to be increasingly incorporated into the routine care of patients with AML. Full article

The arrangement and design of urban streets have a profound impact on the thermal conditions within cities, including the mitigation of excessive street land surface temperatures (LSTs). However, previous research has mainly addressed the linear relationships between the physical spatial elements of streets and LST. There has been limited exploration of potential nonlinear relationships and the influence of population density variations. This study explores multi-dimensional street composition indicators obtained from street-view imagery and applies generalized additive models (GAMs) and geographically weighted regression (GWR) to evaluate the indicators’ impact on LST in areas with various population densities. The results indicate the following: (1) The six indicators—green space index (GSI), tree canopy index (TCI), sky open index (SOI), spatial enclosure index (SEI), road width index (RWI), and street walking index (SWI)—all have significant nonlinear effects on summer daytime LST. (2) Among all categories, the GSI negatively affects LST. Moreover, the TCI’s impact on LST shifts from negative to positive as its value increases. The SOI and SWI positively affect LST in all categories. The SEI’s effect on LST changes from negative to positive in the total and high-population (HP) categories, and it remains negative in the low-population (LP) category. The RWI positively affects LST in the total category, shifts from negative to positive in the LP category, and remains negative in the HP category. (3) The influence ranking is GSI > SEI > SWI > SOI > TCI > RWI, with GSI being the most significant factor. These findings provide key insights for mitigating street LSTs through design interventions, contributing to sustainable urban development. Full article

The reshaping of End-of-Life (EoL) components by means of the sheet metal forming process has been considered largely attractive, even from the social and economic point of view. At the same time, EoL parts can often be characterized by non-uniform thicknesses or alternation of work-hardened/undeformed zones as the result of the manufacturing process. Such heterogeneity can hinder a proper reshaping of the EoL part, and residual marks on the reformed blanks can still be present at the end of the reshaping step. In a previous analysis, the authors evaluated the effectiveness of reshaping a blank with a deep-drawn feature by means of the Sheet Hydroforming (SHF) process: it was demonstrated that residual marks were still present if the deep-drawn feature was located in a region not enough strained during the reshaping step. Starting from this condition and adopting a numerical approach, additional investigations were carried out, changing the profile of the load applied by the blank holder and the maximum oil pressure. Numerical results were collected in terms of overall strain severity and residual height of the residual marks from the deep-drawn feature at the end of the reshaping step. Data were then fitted by accurate Response Surfaces trained by means of interpolant Radial Basis Functions and anisotropic Kriging algorithms, subsequently used to carry out a virtual optimization managed by multi-objective evolutionary algorithms (MOGA-II and NSGA-II). Optimization results, subsequently validated via experimental trials, provided the optimal working conditions to achieve a remarkable reduction of the marks from the deep-drawn feature without the occurrence of rupture. Full article

In this study, the potential of employing SiN_x (silicon nitride) waveguide platforms to enable the use of liquid-crystal-based phase shifters for on-chip optical modulators was thoroughly investigated using 3D-FDTD (3D finite-difference time-domain) simulations. The entire structure of liquid-crystal-based Mach–Zehnder interferometer (MZI) optical modulators, consisting of multi-mode interferometer splitters, different tapering sections, and liquid-crystal-based phase shifters, was systematically and holistically investigated with a view to developing a compact, wideband, and CMOS-compatible (complementary metal-oxide semiconductor) bias voltage optical modulator with competitive modulation efficiency, good fabrication tolerance, and single-mode operation using the same SiN_x waveguide layer for the entire device. The trade-off between several important parameters is critically discussed in order to reach a conclusion on the possible optimized parameter sets. Contrary to previous demonstrations, this investigation focused on the potential of achieving such an optical device using the same SiN_x waveguide layer for the entire device, including both the passive and active regions. Significantly, we show that it is necessary to carefully select the phase shifter length of the LC-based (liquid crystal) MZI optical modulator, as the phase shifter length required to obtain a π phase shift could be as low as a few tens of microns; therefore, a phase shifter length that is too long can contradictorily worsen the optical modulation. Full article