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As the demand for health grows, the increase in medical waste generation is gradually outstripping the load. In this paper, we propose a deep learning approach for identification and classification of medical waste. Deep learning is currently the most popular technique in image classification, but its need for large amounts of data limits its usage. In this scenario, we propose a deep learning-based classification method, in which ResNeXt is a suitable deep neural network for practical implementation, followed by transfer learning methods to improve classification results. We pay special attention to the problem of medical waste classification, which needs to be solved urgently in the current environmental protection context. We applied the technique to 3480 images and succeeded in correctly identifying 8 kinds of medical waste with an accuracy of 97.2%; the average F1-score of five-fold cross-validation was 97.2%. This study provided a deep learning-based method for automatic detection and classification of 8 kinds of medical waste with high accuracy and average precision. We believe that the power of artificial intelligence could be harnessed in products that would facilitate medical waste classification and could become widely available throughout China.

Seismic anisotropy tomography is the updated geophysical imaging technology that can reveal 3-D variations of both structural heterogeneity and seismic anisotropy, providing unique constraints on geodynamic processes in the Earth’s crust and mantle. Here we introduce recent advances in the theory and application of seismic anisotropy tomography, thanks to abundant and high-quality data sets recorded by dense seismic networks deployed in many regions in the past decades. Applications of the novel techniques led to new discoveries in the 3-D structure and dynamics of subduction zones and continental regions. The most significant findings are constraints on seismic anisotropy in the subducting slabs. Fast-velocity directions (FVDs) of azimuthal anisotropy in the slabs are generally trench-parallel, reflecting fossil lattice-preferred orientation of aligned anisotropic minerals and/or shape-preferred orientation due to transform faults produced at the mid-ocean ridge and intraslab hydrated faults formed at the outer-rise area near the oceanic trench. The slab deformation may play an important role in both mantle flow and intraslab fabric. Trench-parallel anisotropy in the forearc has been widely observed by shear-wave splitting measurements, which may result, at least partly, from the intraslab deformation due to outer-rise yielding of the incoming oceanic plate. In the mantle wedge beneath the volcanic front and back-arc areas, FVDs are trench-normal, reflecting subduction-driven corner flows. Trench-normal FVDs are also revealed in the subslab mantle, which may reflect asthenospheric shear deformation caused by the overlying slab subduction. Toroidal mantle flow is observed in and around a slab edge or slab window. Significant azimuthal and radial anisotropies occur in the big mantle wedge beneath East Asia, reflecting hot and wet upwelling flows as well as horizontal flows associated with deep subduction of the western Pacific plate and its stagnation in the mantle transition zone. The geodynamic processes in the big mantle wedge have caused craton destruction, back-arc spreading, and intraplate seismic and volcanic activities. Ductile flow in the middle-lower crust is clearly revealed as prominent seismic anisotropy beneath the Tibetan Plateau, which affects the generation of large crustal earthquakes and mountain buildings.

A detailed description of wake characteristics is essential for optimizing wind farm performance. Compared with the wake of a stand-alone wind turbine, less attention has been paid to wind turbine wakes in large wind farms. In this work, we investigate the vertical position of wakes for wind turbines in large wind farms with different streamwise turbine spacings and ground roughness lengths using large-eddy simulation with an actuator disk model. The simulation results reveal an upward shift of the wake center (defined as the position with the maximum velocity deficit) for the wind turbine deeply arrayed in the wind farm. Larger upward shifts of the wake center are observed for wind turbines in further downstream rows and wind turbines installed on the ground with higher roughness, for which the wake expands at a higher rate. It is conjectured that the upward shift of the wake center is caused by the upward shift of the turbulence-dominated momentum entrainment region and the constraint of ground on wake expansion. An analytical wake model incorporating the upward-shifting wake center was developed. In the proposed model, different expansion rates are employed for the lower and upper wake regions. The upward shift of the wake center is directly taken into account using the large-eddy simulation results. The comparison with the large-eddy simulation results demonstrates the importance of accounting for the upward shift of the wake center in analytical wake models.

The ability to dynamically organize functional nanoparticles (NPs) via the use of environmental triggers (temperature, pH, light, or solvent polarity) opens up important perspectives for rapid and convenient construction of a rich variety of complex assemblies and materials with new structures and functionalities. Here, we report an unconventional strategy for crafting stable hairy NPs with light-enabled reversible and reliable self-assembly and tunable optical properties. Central to our strategy is to judiciously design amphiphilic star-like diblock copolymers comprising inner hydrophilic blocks and outer hydrophobic photoresponsive blocks as nanoreactors to direct the synthesis of monodisperse plasmonic NPs intimately and permanently capped with photoresponsive polymers. The size and shape of hairy NPs can be precisely tailored by modulating the length of inner hydrophilic block of star-like diblock copolymers. The perpetual anchoring of photoresponsive polymers on the NP surface renders the attractive feature of self-assembly and disassembly of NPs on demand using light of different wavelengths, as revealed by tunable surface plasmon resonance absorption of NPs and the reversible transformation of NPs between their dispersed and aggregated states. The dye encapsulation/release studies manifested that such photoresponsive NPs may be exploited as smart guest molecule nanocarriers. By extension, the star-like block copolymer strategy enables the crafting of a family of stable stimuli-responsive NPs (e.g., temperature- or pH-sensitive polymer-capped magnetic, ferroelectric, upconversion, or semiconducting NPs) and their assemblies for fundamental research in self-assembly and crystallization kinetics of NPs as well as potential applications in optics, optoelectronics, magnetic technologies, sensory materials and devices, catalysis, nanotechnology, and biotechnology.

Forest fire is a ubiquitous disaster which has a long-term impact on the local climate as well as the ecological balance and fire products based on remote sensing satellite data have developed rapidly. However, the early forest fire smoke in remote sensing images is small in area and easily confused by clouds and fog, which makes it difficult to be identified. Too many redundant frequency bands and remote sensing index for remote sensing satellite data will have an interference on wildfire smoke detection, resulting in a decline in detection accuracy and detection efficiency for wildfire smoke. To solve these problems, this study analyzed the sensitivity of remote sensing satellite data and remote sensing index used for wildfire detection. First, a high-resolution remote sensing multispectral image dataset of forest fire smoke, containing different years, seasons, regions and land cover, was established. Then Smoke-Unet, a smoke segmentation network model based on an improved Unet combined with the attention mechanism and residual block, was proposed. Furthermore, in order to reduce data redundancy and improve the recognition accuracy of the algorithm, the conclusion was made by experiments that the RGB, SWIR2 and AOD bands are sensitive to smoke recognition in Landsat-8 images. The experimental results show that the smoke pixel accuracy rate using the proposed Smoke-Unet is 3.1% higher than that of Unet, which could effectively segment the smoke pixels in remote sensing images. This proposed method under the RGB, SWIR2 and AOD bands can help to segment smoke by using high-sensitivity band and remote sensing index and makes an early alarm of forest fire smoke.

Haerwusu Surface Mine of the Jungar Coalfield is a large coal mine. Its coal formation environments have not been reported in detail. In order to reconstruct the paleoenvironment of the peat formation, nine samples were collected, and were analyzed using microscope, column chromatography, gas chromatography (GC), and gas chromatography–mass spectrometry (GC–MS). According to the results of the microscopic analysis, the average random vitrinite reflectance (Rr) is 0.73%, indicating a low rank bituminous coal. Vitrinite group is the predominant macerals with an average content of 54.54%, followed by inertinite group with an average of 35.99%. The higher inertinite contents indicate widespread wildfire events during the peat formation. The distribution pattern of n-alkanes, the cross plot between Pr/n-C17 and Ph/n-C18, the lower saturated/aromatic hydrocarbon ratios (0.22–0.68) and the presence of cadalene, retene, simonellite indicates that the organic matter is predominantly terrestrial higher plants with a small amount of aquatic organisms. The ternary diagram of Pr/Ph, Pr/n-C17 and Ph/n-C18 and the relative abundance of fluorene, dibenzofuran and dibenzothiophene indicate a continental–oceanic alternative facies. The higher contents of combustion-derived PAHs are also indicative of widespread wildfire events during peat formation.

Symmetry in vehicle body morphology is a crucial factor for achieving visual sensory balance in users, and it also serves as an important method for enhancing the efficiency of vehicle body research and development. This study proposes an AHP-SD-TOPSIS-AIGC integrated morphological design method to address multi-factorial design complexities in new energy commercial vehicle body styling under emotion-driven frameworks. Through literature retrieval and survey analysis, a Kansei evaluation system was constructed, with hierarchical design indicators established via Analytic Hierarchy Process (AHP) and weights determined through consistency matrices. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) identified optimal style forms exhibiting high emotional intention coupling, while edge detection algorithms extracted symmetrical spline features for body contour modeling. Artificial Intelligence Generated Content (AIGC) tools subsequently generated innovative solutions, validated through truck design applications to confirm method rationality and effectiveness. The results of the study show that the styling elements are accurately matched to user preferences and can identify target improvement points, and that the method can effectively achieve the output of the proposal for the design of commercial vehicle body morphology and is also applicable to passenger car-type vehicles to achieve the adaptation of multi-intentional emotional design.

Inland crustal earthquakes usually occur in the brittle upper crust (0–20 km depths), but the 6 September 2018 Eastern Iburi earthquake (M 6.7) took place in southern Hokkaido with a focal depth of ~37 km, causing 41 fatalities and serious damage to the local infrastructure. The reason why this event was so deep and its causal mechanism are still unclear. In this work we study the three-dimensional P and S wave seismic attenuation (1/Q) structure in the source zone of the 2018 Iburi earthquake. Our results show that this event occurred at the boundary between the Sorachi-Yezo belt (low Q) and the dipping Northeastern (NE) Japan arc (high Q) that is descending beneath the Kuril arc. The collision between the NE Japan and Kuril arcs as well as fluids from dehydration of the subducting Pacific plate caused this big event and its unusual focal depth. Similar attenuation structures are revealed in source zones of the 1970 Hidaka earthquake (M 6.7) and the 1982 Urakawa-oki earthquake (M 7.1), suggesting that they were caused by similar processes. We think that large earthquakes will take place again on the active thrust faults in southern Hokkaido in the coming decades. Hence, we should pay much attention to the seismic risk and prepare for reduction of earthquake hazards there.

Surface-enhanced Raman spectroscopy (SERS) is a powerful technique for the discrimination, identification, and potential quantification of certain compounds/organisms. However, its real application is challenging due to the multiple interference from the complicated detection matrix. Therefore, selective/specific detection is crucial for the real application of SERS technique. We summarize in this review five selective/specific detection techniques (chemical reaction, antibody, aptamer, molecularly imprinted polymers and microfluidics), which can be applied for the rapid and reliable selective/specific detection when coupled with SERS technique.

The 180° curvature of the Banda arc at the eastern end of the Java‐Banda subduction zone reflects complicated geodynamic processes. A detailed investigation of its anisotropic structure would reveal its subduction dynamics, further resolving the controversial issue on how the highly arcuate Banda arc formed. We apply tilting‐axis anisotropic tomography to obtain a high‐resolution 3‐D anisotropic model beneath the Java‐Banda region. Our results show significant differences between Java and Banda in the pattern of anisotropy in both the subducting slab and its surrounding mantle, which reflect two distinctly different deformation modes in the two domains. Our results support the single‐slab subduction model for the Banda region. In addition, trench‐normal and upright fast‐velocity‐planes appear in the deep upper mantle, which may indicate material migrations in the big mantle wedge. Fast‐velocity‐planes in the shallow mantle exhibit a toroidal distribution, reflecting past counter‐clockwise rotation and asthenospheric material extrusion. Plain Language Summary How the great curvature of the Banda arc formed remains in debate. Previous studies have proposed a single slab model and a double slab model for this subduction zone. Seismic anisotropy in the subducting slab and its surrounding mantle could provide clues on which model is correct. In this work, we perform the first tomographic inversion for tilting‐axis anisotropy to reveal the 3‐D anisotropic structure of the Java‐Banda subduction zone. The anisotropic features in the subducted slab and its surrounding mantle revealed by our anisotropic tomography support the single‐slab subduction model for the formation of the Banda arc. Key Points Significant anisotropic differences exist between Java and Banda in both the mantle and slab Slab subduction is the main cause of seismic anisotropy in the Java‐Banda arc Our results support the single slab model for the Banda subduction zone