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The effective integration of ecosystem services (ESs) and urban growth simulation is crucial for spatial planning decisions that support sustainable and high-quality development. Interactions (i.e., trade-offs, synergies, and irrelevance) generally exist among ESs, however, few studies have been done on the methodology for simulating urban growth that optimizes the synergistic effect of ESs. In this study, the Wuhan metropolitan area was taken as the research area. Four key ESs in 2000, 2010, and 2020: water yield (WY), carbon storage (CS), habitat quality (HQ), and soil retention (SR) were evaluated by using the InVEST model. Then the ordered weighted averaging (OWA) method was applied to optimally allocate ESs trade-offs and identify ecological priority protection areas (EPPAs). Finally, the PLUS model was used to simulate urban growth scenarios under different levels of ESs protection constraints and propose spatial planning strategies. The results revealed the following. (1) From 2000 to 2020, the three ESs (i.e., WY, CS, and HQ) exhibited the spatial distribution pattern with high in the east and low in the west, and higher in the woodland than in built-up areas, except SR, which remained at a weak level throughout the region. (2) During the 20 years, SR and the other three ESs, HQ and CS showed a synergistic relationship; WY exhibited a very weak trade-off with HQ; and WY and CS changed from a very weak trade-off to a weak synergy. (3) Seven ecosystem service patterns (ESPs) were generated, and ESP 5 was compared as the ideal option. The weights of SR, WY, CS, and HQ in this pattern were 0.063, 0.187, 0.313, and 0.437, respectively. (4) By 2030, the built-up areas will exhibit edge expansion and interconnection development trends, with the high-constraint ecological protection scenario as the most compact mode, and the spatial structure of “two districts, one axis, four cities as one” is consistent with the development tendency. Our findings can help to enrich the method of urban growth management oriented by ecological protection, and provide a scientific basis for ecological environment restoration and land use spatial planning practices.

Abstract Volatile solid additives have attracted increasing attention in optimizing the morphology and improving the performance of currently dominated non‐fullerene acceptor‐based organic solar cells (OSCs). However, the underlying principles governing the rational design of volatile solid additives remain elusive. Herein, a series of efficient volatile solid additives are successfully developed by the crossbreeding effect of chalcogenation and iodination for optimizing the morphology and improving the photovoltaic performances of OSCs. Five benzene derivatives of 1,4‐dimethoxybenzene (DOB), 1‐iodo‐4‐methoxybenzene (OIB), 1‐iodo‐4‐methylthiobenzene (SIB), 1,4‐dimethylthiobenzene (DSB) and 1,4‐diiodobenzene (DIB) are systematically studied, where the widely used DIB is used as the reference. The effect of chalcogenation and iodination on the overall property is comprehensively investigated, which indicates that the versatile functional groups provided various types of noncovalent interactions with the host materials for modulating the morphology. Among them, SIB with the combination of sulphuration and iodination enabled more appropriate interactions with the host blend, giving rise to a highly ordered molecular packing and more favorable morphology. As a result, the binary OSCs based on PM6:L8‐BO and PBTz‐F:L8‐BO as well as the ternary OSCs based on PBTz‐F:PM6:L8‐BO achieved impressive high PCEs of 18.87%, 18.81% and 19.68%, respectively, which are among the highest values for OSCs.

Urban agglomerations are important spatial carriers for regional economic development, but sustained urban expansion has triggered a series of issues such as conflicts between “people and land” and “built-up areas and green spaces”. However, many studies have rarely incorporated spatiotemporal patterns and driving mechanism into scenario simulation of urban growth, and has also overlooked subsequent discussions on the specific integration of simulation results with spatial planning. Considering the case of Min Delta Region, this study analyzes the spatiotemporal evolution of urban expansion based on multi-source data and explore the influential driving mechanism using Random forest algorithm. A combination of Markov and FLUS models was used to dynamically simulate future urban growth patterns. The results showed that 1) From 1995 to 2015, the urban expansion increased by 2.26 times, showing a trend of first accelerating and then slowing down. It was also discovered that there is an anisotropy toward expansion in the trajectory of urban growth. 2) With marginal increase and enclave increase, general urban expansion was observed to be changing from diffusion to agglomeration, and the urban expansion hotspots mainly located in the southeast coastal area. 3) Humanistic links, geographic position, social and economic considerations all played a role in the development of the Min Delta region, and can be summarized as the “four forces” driving mechanism model. 4) By 2035, the trend of continuous development with the Min Delta built-up area as the main body will be basically formed, and incorporating ecological restrictions can effectively restrain urban growth's encroachment into ecological spaces. Based on the simulation results, Min Delta region gradually showing a spatial structure evolution trend from the single core to the dual core mode, and then to the multi-center and networked mode. This study would serve as a multi-angle decision-making reference for regional spatial and ecological protection planning and urban growth management, and as a scientific foundation for high-quality development planning in the region.

Fire and smoke detection is crucial for the safe mining of coal energy, but previous fire-smoke detection models did not strike a perfect balance between complexity and accuracy, which makes it difficult to deploy efficient fire-smoke detection in coal mines with limited computational resources. Therefore, we improve the current advanced object detection model YOLOv8s based on two core ideas: (1) we reduce the model computational complexity and ensure real-time detection by applying faster convolutions to the backbone and neck parts; (2) to strengthen the model's detection accuracy, we integrate attention mechanisms into both the backbone and head components. In addition, we improve the model's generalization capacity by augmenting the data. Our method has 23.0% and 26.4% fewer parameters and FLOPs (Floating-Point Operations) than YOLOv8s, which means that we have effectively reduced the computational complexity. Our model also achieves a mAP (mean Average Precision) of 91.0%, which is 2.5% higher than the baseline model. These results show that our method can improve the detection accuracy while reducing complexity, making it more suitable for real-time fire-smoke detection in resource-constrained environments.

Microfluidic devices are composed of microchannels with a diameter ranging from ten to a few hundred micrometers. Thus, quite a small (10 ^−9 –10 ^−18 l) amount of liquid can be manipulated by such a precise system. In the past three decades, significant progress in materials science, microfabrication, and various applications has boosted the development of promising functional microfluidic devices. In this review, the recent progress on novel microfluidic devices with various functions and applications is presented. First, the theory and numerical methods for studying the performance of microfluidic devices are briefly introduced. Then, materials and fabrication methods of functional microfluidic devices are summarized. Next, the recent significant advances in applications of microfluidic devices are highlighted, including heat sinks, clean water production, chemical reactions, sensors, biomedicine, capillaric circuits, wearable electronic devices, and microrobotics. Finally, perspectives on the challenges and future developments of functional microfluidic devices are presented. This review aims to inspire researchers from various fields—engineering, materials, chemistry, mathematics, physics, and more—to collaborate and drive forward the development and applications of functional microfluidic devices, specifically for achieving carbon neutrality.

Abstract Breast cancer is the most common female malignancy worldwide. Ubiquitin-specific peptidase 53 (USP53) has been shown to exert cancer-suppressing functions in several solid tumors, but its role and the underlying mechanism in breast cancer has not been clearly elucidated. Therefore, we have carried out a series of detailed studies on this matter at the levels of bioinformatics, clinical tissue, cell function and animal model. We found that USP53 expression was downregulated in breast cancer specimens and was negatively correlated with the clinical stages. Gain- and loss-of-function experiments demonstrated USP53 inhibited proliferation, clonogenesis, cell cycle and xenograft growth, as well as induced apoptosis and mitochondrial damage of breast cancer cells. Co-immunoprecipitation data suggested that USP53 interacted with zinc finger MYND-type containing 11 (ZMYND11), and catalyzed its deubiquitination and stabilization. The 33–50 amino acid Cys-box domain was key for USP53 enzyme activity, but not essential for its binding with ZMYND11. The rescue experiments revealed that the anti-tumor role of USP53 in breast cancer cells was at least partially mediated by ZMYND11. Both USP53 and ZMYND11 were prognostic protective factors for breast cancer. USP53-ZMYND11 axis may be a good potential biomarker or therapeutic target for breast cancer, which can provide novel insights into the diagnosis, treatment and prognosis.

Corn is one of the global staple grains and the largest grain crop in China. During harvesting, grain separation, and corn production, corn is susceptible to mechanical damage including surface cracks, internal cracks, and breakage. However, the internal cracks are difficult to observe. In this study, hyperspectral imaging was used to detect mechanical damage in corn seeds. The corn seeds were divided into four categories that included intact, broken, internally cracked, and surface-crackedtv. This study compared three feature extraction methods, including principal component analysis (PCA), kernel PCA (KPCA), and factor analysis (FA), as well as a joint feature extraction method consisting of a combination of these methods. The dimensionality reduction results of the three methods (FA + KPCA, KPCA + FA, and PCA + FA) were combined to form a new combined dataset and improve the classification. We then compared the effects of six classification models (ResNet, ShuffleNet-V2, MobileNet-V3, ResNeSt, EfficientNet-V2, and MobileNet-V4) and proposed a ResNeSt_E network based on the ResNeSt and efficient multi-scale attention modules. The accuracy of ResNeSt_E reached 99.0%, and this was 0.4% higher than that of EfficientNet-V2 and 0.7% higher than that of ResNeSt. Additionally, the number of parameters and memory requirements were reduced and the frames per second were improved. We compared two dimensionality reduction methods: KPCA + FA and PCA + FA. The classification accuracies of the two methods were the same; however, PCA + FA was much more efficient than KPCA + FA and was more suitable for practical detection. The ResNeSt_E network could detect both internal and surface cracks in corn seeds, making it suitable for mobile terminal applications. The results demonstrated that detecting mechanical damage in corn seeds using hyperspectral images was possible. This study provides a reference for mechanical damage detection methods for corn.

In recent years, the simplified computation of position and velocity changes in nonlinear systems using Lie groups and Lie algebra has been widely used in the study of robot localization systems. The unscented Kalman filter (UKF) can effectively deal with nonlinear systems through the unscented transformation, and in order to more accurately describe the robot localization system, the UKF method based on Lie groups has been studied successively. The computational complexity of the UKF on Lie groups is high, and in order to simplify its computation, the Lie groups are applied to the manifold, which efficiently handles the state and uncertainty and ensures that the system maintains the geometric constraints and computational simplicity during the updating process. In this paper, a multi-sensor fusion localization method based on an unscented Kalman filter on manifolds (UKF-M) is investigated. Firstly, a system model and a multi-sensor model are established based on an Autonomous Underwater Vehicle (AUV), and a corresponding UKF-M is designed for the system. Secondly, the multi-sensor fusion method is designed, and the fusion method is applied to the UKF-M. Finally, the proposed method is validated using an underwater cave dataset. The experiments demonstrate that the proposed method is suitable for underwater environments and can significantly correct the cumulative error in the trajectory estimation to achieve accurate underwater localization.

As the key component of a five-axis CNC planer-type milling machine, the integral mechanical property of the A/C swing angle milling head directly affects the machining accuracy and stability of the milling machine. Taking the mechanical A/C swing-angle milling head of a five-axis numerical-control gantry milling machine as the research object, the stress deformation characteristics and natural frequency of the swing-angle milling head under actual working conditions were studied using finite-element analysis. Based on the analytical results, it was determined that the cardan frame, with its large mass proportion and strong rigidity of the whole milling head, is the object to be optimized. The topological optimization of the cardan frame, in which achieving the minimum flexibility was the optimization objective, was carried out to determine the quality reduction area. By comparing the simulation results of the cardan frames of three different rib plate structures, it was shown that the cardan frame performance of the ten-type rib plate structure was optimal. The analytical results showed that, when the cardan frame met the design requirements for stiffness and strength, the mass after optimization was reduced by 13.67% compared with the mass before optimization, the first-order natural frequency was increased by 7.9%, and the maximum response amplitude was reduced in all directions to avoid resonance, which was beneficial to the improvement of the dynamic characteristics of the whole machine. At the same time, the rationality and effectiveness of the lightweight design method of the cardan frame were verified, which has strong engineering practicality. The research results provide an important theoretical basis for the optimization of other machine tool gimbals and have important practical significance and application value.