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Bongkrekic acid is a lethal mitochondrial toxin produced by Burkholderia gladioli pathovar cocovenenans, posing severe threats to food safety due to their high stability and the lack of effective antidotes. Developing specific, high-affinity recognition elements is crucial to overcoming the limitations of current BA detection methods in food matrices, and thereby safeguarding food safety and public health. In this study, we report for the first time the selection and remodelling of a DNA aptamer with high affinity for BA, which could be used as a promising recognition tool for sensitive BA detection in food. Integrating isothermal titration calorimetry, molecular docking, and molecular dynamics simulations revealed that the binding of BA to F3-1 follows an induced-fit mechanism. This study is the first to report a DNA aptamer with nanomolar affinity for BA, clarify its underlying binding mechanism, and provide a reliable recognition element for sensitive and specific BA detection in food samples.

To address the challenges of detecting small targets in complex underwater environments, an efficient and lightweight model, SFESI-YOLOv8n, is proposed. The model improves small target recognition by incorporating a dedicated detection layer and reduces parameter count by removing the large target detection layer. Furthermore, the introduction of the C2f-F module eliminates redundant information from consecutive convolution operations in the bottleneck, further simplifying the model. The integration of a lightweight mixed local context attention (MLCA) mechanism within the small target fusion layer increases sensitivity to small targets. The dynamic upsampler (DySample) employs point sampling to preserve enhanced edge and detail information in feature maps, resulting in clearer feature representations. In addition, the novel In-NWD loss function, utilizing Wasserstein distance and auxiliary bounding boxes, improves small target detection performance. On the UPRC2020 public dataset, SFESI-YOLOv8n achieved an mAP@0.5 of 83.7%, which is a 1.1% improvement over the baseline model. The parameter count and size of the model were reduced by 49.2% and 45.7%, respectively. The frame rate reached 227 FPS, indicating a 9 FPS increase compared to the baseline. On the NVIDIA Jetson TX2 edge device, inference latency decreased from 65 ms to 24 ms with TensorRT acceleration, thereby meeting real-time detection requirements. The SFESI-YOLOv8n model provides a viable and efficient solution for the autonomous detection of small underwater targets, demonstrating significant practical value.

Photoelectrons ionized from atoms and molecules in a strong laser field are either emitted directly or rescattered by the nucleus, both of which can serve as efficiently useful tools for molecular orbital imaging. We measure the photoelectron angular distributions of molecules (N 2 , O 2 and CO 2 ) ionized by infrared laser pulses (1320 nm, 0.2 ~ 1 × 10 14  W/cm 2 ) from multiphoton to tunneling regime and observe an enhancement of interference stripes in the tunneling regime. Using a semiclassical rescattering model with implementing the interference effect, we show that the enhancement arises from the sub-laser-cycle holographic interference of the contributions of the back-rescattering and the non-rescattering electron trajectory. It is shown that the low-energy backscattering photoelectron interference patterns have encoded the structural information of the molecular initial orbitals and attosecond time-resolved dynamics of photoelectron, opening new paths in high-resolution imaging of sub-Ångström and sub-femtosecond structural dynamics in molecules.

We used the latest database of Chinese industrial enterprises to make an empirical test of the relationship between the SO2 emissions trading pilot(ETP)policy implemented in 2007 and enterprise performance based on a difference-in-difference(DID)method since the ETP policy tends to be a \"quasi-natural experiment.\" The empirical results show that the ETP policy has a significant promotion effect on enterprise performance, which provides evidence supporting the \"Porter hypothesis\" in China.Heterogeneous regression results show that ETP policies play a vital role in promoting development in heavily polluting industries, state-owned enterprises, and central regions.The test results of the mechanism demonstrate that the ETP policy has two mechanisms to affect enterprise performance: \"improving the total factor productivity of the enterprise\" and \"increasing the extra cost of the enterprise.\" There are two policy implications of our research: first, government departments should strive to explore and implement relevant market-based environmental regulations and policies; second, government departments should vigorously support small and medium-sized enterprises and backward areas in the west while focusing on heavily polluting industries and making the best use of environmental regulations in pollution control, which are the key points for China to win the defense of the blue sky.

Shrimp diseases are one of the primary causes of economic losses in shrimp aquaculture. To prevent disease transmission and enhance intelligent detection efficiency in shrimp farming, this paper proposes a lightweight network architecture based on YOLOv8n. First, by designing the RLDD detection head and C2f-EMCM module, the model reduces computational complexity while maintaining detection accuracy, improving computational efficiency. Subsequently, an improved SegNext_Attention self-attention mechanism is introduced to further enhance the model's feature extraction capability, enabling more precise identification of disease characteristics. Extensive experiments, including ablation studies and comparative evaluations, are conducted on a self-constructed shrimp disease dataset, with generalization tests extended to the URPC2020 dataset. Results demonstrate that the proposed model achieves a 32.3% reduction in parameters compared to the original YOLOv8n, with a mAP@0.5 of 92.7% (3% improvement over YOLOv8n). Additionally, the model outperforms other lightweight YOLO-series models in mAP@0.5, parameter count, and model size. Generalization experiments on the URPC2020 dataset further validate the model's robustness, showing a 4.1% increase in mAP@0.5 compared to YOLOv8n. The proposed method achieves an optimal balance between accuracy and efficiency, providing reliable technical support for intelligent disease detection in shrimp aquaculture.

Early detection and diagnosis of diabetic retinopathy is one of the current research focuses in ophthalmology. However, due to the subtle features of micro-lesions and their susceptibility to background interference, ex-isting detection methods still face many challenges in terms of accuracy and robustness. To address these issues, a lightweight and high-precision detection model based on the improved YOLOv8n, named YOLO-KFG, is proposed. Firstly, a new dynamic convolution KWConv and C2f-KW module are designed to improve the backbone network, enhancing the model's ability to perceive micro-lesions. Secondly, a fea-ture-focused diffusion pyramid network FDPN is designed to fully integrate multi-scale context information, further improving the model's ability to perceive micro-lesions. Finally, a lightweight shared detection head GSDHead is designed to reduce the model's parameter count, making it more deployable on re-source-constrained devices. Experimental results show that compared with the base model YOLOv8n, the improved model reduces the parameter count by 20.7%, increases mAP@0.5 by 4.1%, and improves the recall rate by 7.9%. Compared with single-stage mainstream algorithms such as YOLOv5n and YOLOv10n, YOLO-KFG demonstrates significant advantages in both detection accuracy and efficiency.

This paper, based on the literature review and field investigation, studied the status quo of maker spaces in main urban district of Chengdu at a time when the development of the maker spaces has received strong support from national and local governments. In addition to introducing the related concepts and policy support of the maker space, it summarized its distribution characteristics, audience characteristics, and status quo as well as problems and presented the direction for the development of the maker space in main urban area of Chengdu grounded on a survey of the status quo of national maker spaces in main urban area of Chengdu.

ObjectivesThis study aimed to investigate the knowledge, attitudes and practices (KAP) regarding dysphagia and its care among patients with dysphagia and their caregivers.DesignA cross-sectional survey.SettingThis multicentre cross-sectional study was conducted between May and September 2024. It was led by the China-Japan Friendship Hospital and involved multiple institutions, including nursing communities, nursing homes and community hospitals, located in both Beijing and Shandong Province.ParticipantsPatients with clinically diagnosed dysphagia and their caregivers recruited from the China-Japan Friendship Hospital, nursing communities, nursing homes and community hospitals.Primary and secondary outcome measuresData were collected through a self-designed questionnaire encompassing sociodemographic characteristics and three dimensions of KAP. The primary outcome was the KAP scores. Secondary outcomes included the interaction between the three KAP dimensions.ResultsA total of 416 participants were included in the final analysis, of whom 317 (76.2%) were female. The mean scores for KAP were 12.02±8.12 (possible range: 0–24), 31.38±4.77 (possible range: 8–40) and 29.29±9.03 (possible range: 8–40), respectively. Correlation analysis indicated significant positive relationships between knowledge and attitudes (r=0.416, p=0.002), knowledge and practices (r=0.412, p<0.001), and attitudes and practices (r=0.499, p<0.001). The structural equation modelling showed that knowledge directly influenced attitudes (β=0.483, p<0.001) and practices (β=0.276, p<0.001). Attitudes also had a direct impact on practices (β=0.310, p<0.001), while knowledge indirectly affected practices through attitudes (β=0.150, p<0.001).ConclusionsPatients with dysphagia and their caregivers demonstrated inadequate knowledge but generally positive attitudes and proactive practices towards dysphagia and its care. Adequate knowledge might be correlated with better attitudes and practices.

Pedestrian-positioning technology based on the foot-mounted micro inertial measurement unit (MIMU) plays an important role in the field of indoor navigation and has received extensive attention in recent years. However, the positioning accuracy of the inertial-based pedestrian-positioning method is rapidly reduced because of the relatively low measurement accuracy of the measurement sensor. The zero-velocity update (ZUPT) is an error correction method which was proposed to solve the cumulative error because, on a regular basis, the foot is stationary during the ordinary gait; this is intended to reduce the position error growth of the system. However, the traditional ZUPT has poor performance because the time of foot touchdown is short when the pedestrians move faster, which decreases the positioning accuracy. Considering these problems, a forward and reverse calculation method based on the adaptive zero-velocity interval adjustment for the foot-mounted MIMU location method is proposed in this paper. To solve the inaccuracy of the zero-velocity interval detector during fast pedestrian movement where the contact time of the foot on the ground is short, an adaptive zero-velocity interval detection algorithm based on fuzzy logic reasoning is presented in this paper. In addition, to improve the effectiveness of the ZUPT algorithm, forward and reverse multiple solutions are presented. Finally, with the basic principles and derivation process of this method, the MTi-G710 produced by the XSENS company is used to complete the test. The experimental results verify the correctness and applicability of the proposed method.

Although China produces 1.1 billion tons of coal per year, demand is projected at almost 1.6 billion tons in 2000. Transport bottlenecks, coal and electricity shortages, and worsening air pollution threaten the country's double-digit GNP growth. To address these problems, the World Bank and the Chinese State Planning Commission developed a decision support system consisting of a mixed-integer program, a geographic information system, and related submodels. The Coal Transport Study (CTS) model covers coal mining, washing, and transport; thermal, hydro, and nuclear power generation; electricity transmission; pollution levels; and scrubbers; which together will require at least $240 billion in new investments over a 15-year horizon. The analysis results influenced several government policies concerning GNP growth, coal imports, and various capital investments, with a potential benefit of about $6.4 billion from 1991 to 2005.