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Using deep learning-based object detection algorithms for landslide hazards detection is very popular and effective. However, most existing algorithms are designed for landslides in a specific geographical range. This paper constructs a set of landslide detection models YOLOX-Pro, based on the improved YOLOX (You Only Look Once) target detection model to address the poor detection of complex mixed landslides. Wherein the VariFocal is used to replace the binary cross entropy in the original classification loss function to solve the uneven distribution of landslide samples and improve the detection recall; the coordinate attention (CA) mechanism is added to enhance the detection accuracy. Firstly, 1200 historical landslide optical remote sensing images in thirty-eight areas of China were extracted from Google Earth to create a mixed sample set for landslide detection. Next, the three attention mechanisms were compared to form the YOLOX-Pro model. Then, we tested the performance of YOLOX-Pro by comparing it with four models: YOLOX, YOLOv5, Faster R-CNN, and Single Shot MultiBox Detector (SSD). The results show that the YOLOX-Pro(m) has significantly improved the detection accuracy of complex and small landslides than the other models, with an average precision (AP0.75) of 51.5%, APsmall of 36.50%, and ARsmall of 49.50%. In addition, optical remote sensing images of a 12.32 km2 group-occurring landslides area located in Mibei village, Longchuan County, Guangdong, China, and 750 Unmanned Aerial Vehicle (UAV) images collected from the Internet were also used for landslide detection. The research results proved that the proposed method has strong generalization and good detection performance for many types of landslides, which provide a technical reference for the broad application of landslide detection using UAV.

Dragon fruit stem disease significantly affects both the quality and yield of dragon fruit. Therefore, there is an urgent need for an efficient, high-precision intelligent detection method to address the challenge of disease detection. To address the limitations of traditional methods, including slow detection and weak micro-integration capability, this paper proposes an improved YOLOv8-G algorithm. The algorithm reduces computational redundancy by introducing the C2f-Faster module. The loss function was modified to the structured intersection over union (SIoU), and the coordinate attention (CA) and content-aware reorganization feature extraction (CARAFE) modules were incorporated. These enhancements increased the model’s stability and improved its accuracy in recognizing small targets. Experimental results showed that the YOLOv8-G algorithm achieved a mean average precision (mAP) of 83.1% and mAP50:95 of 48.3%, representing improvements of 3.3% and 2.3%, respectively, compared to the original model. The model size and floating point operations per second (FLOPS) were reduced to 4.9 MB and 6.9 G, respectively, indicating reductions of 20% and 14.8%. The improved model achieves higher accuracy in disease detection while maintaining a lighter weight, serving as a valuable reference for researchers in the field of dragon fruit stem disease detection.

The outbreak and rapid spread of the COVID-19 in December 2019 (Iqbal Z, Aslam MZ, Aslam T, Ashraf R, Kashif M, Nasir H, Register J, 2020, 13, 208–30) has brought great work pressure to nurses on the frontline of the fight against the virus, which is very likely to lead to work deviant behaviors, therefore, how to effectively manage nurses to inhibit their organizational deviance in the context of an emergency public health crisis has a high research value. A questionnaire was administered to 319 Chinese in-service nurses, and SPSS and AMOS software were used to conduct correlation analysis, confirmatory factor analysis, and hierarchical regression analysis to statistically test the hypotheses of the developed model. COVID-19 stress can significantly positively predict nurses’ organizational deviance. The relationship between the two variables is mediated by job satisfaction. Furthermore, perceived organizational support(POS) demonstrates a dual moderating function in our framework: it not only influences the relationship between CST and employee job satisfaction, but also affects the extent to which satisfaction mediates subsequent organizational outcomes. COVID-19 stress is an important psychological factor influencing nurses’ organizational deviance. The government and relevant organizations are supposed to take the psychological stress of such primary medical staff seriously, provide more supportive resources and take various measures to reduce COVID-19 stress to help individuals cope with the COVID-19 crisis.

To address the current steel surface defect detection algorithms in practical applications involving low detection accuracy, an efficient and highly accurate strip steel surface defect detection algorithm, DEW-YOLO, is proposed in this paper. Firstly, by combining the advantages of deformable convolutional networks (DCNs), this paper innovates the C2F module in YOLOv8 and proposes a C2f_DCN module that can flexibly sample features to enhance the abilities of learning and expressing defect features of different sizes and shapes. Secondly, the explicit visual center (EVC) is introduced into the backbone network, which enhances feature extraction capabilities and adaptability and enables the model to better adjust features at different levels and scales. Finally, the original loss function is replaced with the Wise-IoU (WIoU) loss function to accurately measure the similarity between the target frames and improve the defect detection performance of the model. The experimental results on the NEU-DET dataset demonstrate that the algorithms proposed in this paper achieved a mean average precision (mAP) of 80.3% in steel surface defect detection tasks, which was a 3.9% improvement over the original YOLOv8 model. The model’s inference speed reached 91 frames per second (FPS). DEW-YOLO effectively enhances the accuracy of steel defect detection and better satisfies industrial inspection requirements.

Polymerization degree plays a vital role in material properties. Previous methodologies of molecular weight control generally cannot suppress or alleviate batch-to-batch variations in device performance, especially in polymer solar cells. Herein, we develop an in-situ photoluminescence system in tandem with a set of analysis and processing procedures to track and estimate the polymerization degree of organic photovoltaic materials. To support the development of this protocol, we introduce polymer acceptor PYT constructed by near-infrared Y-series small molecule acceptors via Stille polymerization, and shed light on the correlations between molecular weight, spectral parameters, and device efficiencies that enable the design of the optical setup and confirm its feasibility. The universality is verified in PYT derivatives with stereoregularity and fluoro-substitution as well as benzo[1,2-b:4,5-b’]dithiophene-based polymers. Overall, our result provides a tool to tailor suitable conjugated oligomers applied to polymer solar cells and other organic electronics for industrial scalability and desired cost reduction. Polymerization degree plays a vital role in controlling material properties and batch-to-batch variations in device performance of polymer solar cells. Here, authors develop in-situ photoluminescence system in tandem to track and estimate the polymerization degree of organic photovoltaic materials.

Thyroid hormones are metabolic indicators to evaluate the physical condition of emergency hospitalized patients, while the relationship between total triiodothyronine and the severity of emergency inpatients is still unclear. To explore the thyroid function levels of inpatients in emergency ward and the status of combined Nonthyroidal illness syndrome (NTIS), and to emphasize the importance of thyroid hormone examination for non-endocrinology inpatients. According to thyroid function of inpatients in emergency ward, they were divided into NTIS group and non-NTIS group, the hematological characteristics and TH levels of each group were analyzed. Based on clinical diagnoses, the hospitalized patients were divided into three major groups, namely infection group, non-infection group and impaired organ function group. Among them, infection group was further divided into sepsis group, lung infection group and local infection group, altogether five groups. The thyroid function levels and low values in each group were evaluated, and the correlation between hormone levels and inflammatory factors, nutritional indicators and the relationship with the risk of death was discussed. The inpatient rate in emergency ward complicated with NTIS was 62.29%, T3 was the most sensitive index of NTIS, followed by FT3. Compared to non-NTIS group, the NTIS group had an increased risk of death. The sepsis group and impaired organ function group had the highest rates of complicated NTIS, reaching 83.33% and 78.12% respectively. Spearman's correlation analysis implied T3/T4/FT3 levels were positively correlated with ALb and PLT (except T4), and negatively correlated with CRP, D-Dimer, IL-6 and Fer. The Receiver Operating Curve (ROC) and Area under the curve (AUC) showed T3 levels alone were strongly associated with the risk of death (AUC 0.750; 95% CI 0.673–0.828; P  < 0.001). T3 is the most sensitive indicator for emergency patients, followed by FT3. The decrease of T3 level has a good predictive value for mortality risk. Thyroid function should be monitored in critically ill patients.

In complex environments, strawberry disease segmentation models face challenges, such as segmentation difficulties, excessive parameters, and high computational loads, making it difficult for these models to run effectively on devices with limited computational resources. To address the need for efficient running on low-power devices while ensuring effective disease segmentation in complex scenarios, this paper proposes BHI-YOLO, a lightweight instance segmentation model based on YOLOv8n-seg. First, the Universal Inverted Bottleneck (UIB) module is integrated into the backbone network and merged with the C2f module to create the C2f_UIB module; this approach reduces the parameter count while expanding the receptive field. Second, the HS-FPN is introduced to further reduce the parameter count and enhance the model’s ability to fuse features across different levels. Finally, by integrating the Inverted Residual Mobile Block (iRMB) with EMA to design the iRMA, the model is capable of efficiently combining global information to enhance local information. The experimental results demonstrate that the enhanced instance segmentation model for strawberry diseases achieved a mean average precision (mAP@50) of 93%. Compared to YOLOv8, which saw a 2.3% increase in mask mAP, the improved model reduced parameters by 47%, GFLOPs by 20%, and model size by 44.1%, achieving a relatively excellent lightweight effect. This study combines lightweight architecture with enhanced feature fusion, making the model more suitable for deployment on mobile devices, and provides a reference guide for strawberry disease segmentation applications in agricultural environments.

Traditional villages face many difficulties in the era of globalization, especially in light of fast industrialization and urbanization. The breakdown of settlement patterns and the erosion of local characteristics and cultural identities pose critical issues for the sustainable development of these communities. While research on traditional villages and dwellings in core cultural areas is relatively advanced, there remains a significant gap in studies focusing on traditional villages and dwellings in multicultural intermingling regions. By clarifying the characteristics of traditional villages and the cultural landscapes of dwellings under the influence of multiple cultures, as well as their differentiation and underlying mechanisms, this research aims to provide theoretical support for the protective planning of world cultural heritage, which is increasingly characterized by clustering and regionalization. Taking the traditional villages and dwellings in the Jiangxi and Anhui junction area as a case study, we developed a cultural landscape factor system for traditional villages and dwellings across four dimensions: natural environment, spatial configuration, dwelling typology, and historical and cultural context. Using geographic information systems (GIS) zoning methods and statistical spatial analysis, we divided the area into three distinct cultural landscape zones. The findings indicate that the cultural landscapes within each zone exhibit unique regional characteristics at both the village and dwelling levels, particularly in site selection, settlement patterns, and architectural aesthetics. Differentiation across zones is shaped by natural factors, such as topography and water systems, as well as by regional culture, historical migration, the chronological sequence of regional development, commerce and trade growth, and the evolution of administrative systems, alongside broader cultural, economic, and social factors, showing consistent patterns. This study demonstrates that utilizing a scientific and objective zoning approach to accurately identify the cultural landscape characteristics and differentiation patterns across various cultural zones, while clarifying the historical evolution of villages and the transformation of dwelling forms, provides practical insights for cultural landscape zoning in other multicultural regions. Furthermore, it provides scientific guidance to advance China’s rural revitalization strategy and supports the regional protection and sustainable development of world cultural heritage.

Backgrounds & Aims The nutritional evaluation of pancreatic cancer (PC) patients lacks a gold standard or scientific consensus, we aimed to summarize and systematically evaluate the prognostic value of nutritional screening and assessment tools used for PC patients. Methods Relevant studies were retrieved from major databases (PubMed, Embase, Web of Science, Cochrane Library) and searched from January 2010 to December 2023. We performed meta-analyses with STATA 14.0 when three or more studies used the same tool. Results This analysis included 27 articles involving 6,060 PC patients. According to a meta-analysis of these studies, poor nutritional status evaluated using five nutritional screening tools Prognostic Nutritional Index (PNI), Geriatric Nutritional Risk Index (GNRI), Controlling Nutritional Status Score (CONUT), Nutrition Risk Screening (NRS2002) and Glasgow Prognostic Score (GPS) was associated with all-cause mortality in PC patients. But Modified Glasgow Prognostic Score (mGPS) did not. Of all tools analyzed, CONUT had the maximum HR for mortality (HR = 1.978, 95%CI 1.345–2.907, P  = 0.001). Conclusion All-cause mortality in PC patients was predicted by poor nutritional status. CONUT may be the best nutritional assessment tool for PC patients. The clinical application value of Short Form Mini Nutritional Assessment (MNA-SF), Generated Subjective Global Assessment (SGA) and Patient-generated Subjective Global Assessment (PG-SGA) in PC patients need to be confirmed. In order to improve patients’ nutritional status and promote their recovery, nutritional screening tools can be used. Registration This systematic review was registered at the International Prospective Register of Systematic Reviews (PROSPERO) (number CRD42022376715).

To achieve an accurate measurement of tread size after fixed-length cutting, this paper proposes a point-cloud-based tread size measurement method. Firstly, a mathematical model of corner points and a reprojection error is established, and the optimal solution of the number of corner points is determined by the non-dominated sorting genetic algorithm II (NSGA-II), which reduces the reprojection error of the RGB-D camera. Secondly, to address the problem of the low accuracy of the traditional pixel metric ratio measurement method, the random sampling consensus point cloud segmentation algorithm (RANSAC) and the oriented bounding box (OBB) collision detection algorithm are introduced to complete the accurate detection of the tread size. By comparing the absolute error and relative error data of several groups of experiments, the accuracy of the detection method in this paper reaches 1 mm, and the measurement deviation is between 0.14% and 2.67%, which is in line with the highest accuracy standard of the national standard. In summary, the RGB-D visual inspection method constructed in this paper has the characteristics of low cost and high inspection accuracy, which is a potential solution to enhance the pickup guidance of tread size measurement.