Explore the vast range of titles available.

A challenging and attractive task in computer vision is underwater object detection. Although object detection techniques have achieved good performance in general datasets, problems of low visibility and color bias in the complex underwater environment have led to generally poor image quality; besides this, problems with small targets and target aggregation have led to less extractable information, which makes it difficult to achieve satisfactory results. In past research of underwater object detection based on deep learning, most studies have mainly focused on improving detection accuracy by using large networks; the problem of marine underwater lightweight object detection has rarely gotten attention, which has resulted in a large model size and slow detection speed; as such the application of object detection technologies under marine environments needs better real-time and lightweight performance. In view of this, a lightweight underwater object detection method based on the MobileNet v2, You Only Look Once (YOLO) v4 algorithm and attentional feature fusion has been proposed to address this problem, to produce a harmonious balance between accuracy and speediness for target detection in marine environments. In our work, a combination of MobileNet v2 and depth-wise separable convolution is proposed to reduce the number of model parameters and the size of the model. The Modified Attentional Feature Fusion (AFFM) module aims to better fuse semantic and scale-inconsistent features and to improve accuracy. Experiments indicate that the proposed method obtained a mean average precision (mAP) of 81.67% and 92.65% on the PASCAL VOC dataset and the brackish dataset, respectively, and reached a processing speed of 44.22 frame per second (FPS) on the brackish dataset. Moreover, the number of model parameters and the model size were compressed to 16.76% and 19.53% of YOLO v4, respectively, which achieved a good tradeoff between time and accuracy for underwater object detection.

Tropical cyclone rainfall rates have been projected to increase in a warmer climate. The area coverage of tropical cyclones influences their impact on human lives, yet little is known about how tropical cyclone rainfall area will change in the future. Here, using satellite data and global atmospheric model simulations, we show that tropical cyclone rainfall area is controlled primarily by its environmental sea surface temperature (SST) relative to the tropical mean SST (that is, the relative SST), while rainfall rate increases with increasing absolute SST. Our result is consistent with previous numerical simulations that indicated tight relationships between tropical cyclone size and mid-tropospheric relative humidity. Global statistics of tropical cyclone rainfall area are not expected to change markedly under a warmer climate provided that SST change is relatively uniform, implying that increases in total rainfall will be confined to similar size domains with higher rainfall rates. The rainfall rate of tropical cyclones is expected to increase under a warmer climate, yet likely changes in rainfall area remain unknown. Here, the authors combine satellite data and model simulations and show that rainfall area is dependent on relative sea surface temperatures.

Background In response to the contemporary emphasis on promoting adolescents’ physical and mental health, this study adopted a mixed-methods approach to examine the relationship between physical activity on adolescents’ flourishing, tested the mediating roles of self-worth and boredom in this relationship, and further examined the necessity and bottleneck levels of physical activity intensity, duration, and frequency, as well as self-worth and boredom (internal stimulation and external stimulation), for adolescents’ flourishing. Methods Drawing on the core propositions of the Cognitive-Affective Processing System (CAPS), this study constructed a dual-path mediation model to examine how physical activity was associated with adolescents’ flourishing through self-worth and boredom. In this model, self-worth was specified as a primarily cognitive-evaluative mediator, whereas boredom was specified as a primarily affective-experiential mediator. Survey data were collected from 985 adolescents. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to test sufficiency relationships, whereas Necessary Condition Analysis (NCA) was used to examine the necessity and bottleneck levels of key antecedent variables. Results (1) The results showed that physical activity was positively associated with adolescents’ flourishing ( p  < 0.01), positively associated with self-worth ( p  < 0.001), and negatively associated with boredom ( p  < 0.001); (2) As the level of flourishing increased, the antecedent conditions required to achieve higher levels of the outcome also increased, gradually showing a pattern of multi-variable synergy; (3) Mediation analyses further showed that physical activity was associated with adolescents’ flourishing indirectly through both self-worth and boredom. Conclusion Physical activity intensity, duration, and frequency did not emerge as necessary conditions for adolescents’ flourishing. Nevertheless, physical activity showed a positive facilitating association with flourishing. In addition, physical activity was linked to flourishing through both cognitive-evaluative and affective-experiential pathways, indicating that physical activity contributed to higher levels of flourishing by strengthening positive self-evaluation and reducing boredom-related constraints.

Mesoscale convective systems (MCS) contribute about half of the world's precipitation and create flash flooding as well as other extreme weather events. Despite steady progress in research of these systems in the last several decades, better theoretical understanding is still needed to understand their dynamical organizations and to improve their forecasts in numerical models. By using the Moncrieff‐Green horizontal vorticity equation, this paper presents an analytical model of one type of MCS, the steady‐state squall lines. It describes the organization, propagation, and properties of mesoscale circulations of two‐dimensional steady‐state convective systems under sheared environment. Far‐side solutions are formulated to illustrate the underlying physical processes of squall line flows. Numerical procedures are described to solve the model under general environmental conditions. The model leads to the following prediction of squall line properties: Given the environmental profiles of wind and convective available potential energy (CAPE), the propagation speed, the depths and mass fluxes of the tilted ascending front‐to‐rear flow, the overturning updraft, and the descending rear inflow of the squall line can be all determined from the cold pool buoyancy or CAPE. The squall lines are therefore self‐organized dynamical systems that have limited degrees of freedom in their properties. The model advances the theoretical understanding of how mesoscale flow components interact to sustain organized convection. It provides a new tool to interpret squall line systems in high resolution models and to parameterize them in climate models. Plain Language Summary Mesoscale convective systems (MCS) contribute about half of the world's precipitation and create flash flooding as well as other extreme weather events. Their theoretical understanding is still limited. This paper presents a mathematical model of one type of MCS, the steady state two‐dimensional squall lines by building on the theory of Moncrieff and collaborators. The model describes the organization, propagation, circulation properties of squall lines. The formulation leads to prediction of squall line properties: Given the environmental profiles of wind and Convective Available Potential Energy, the propagation speed, the depths and mass fluxes of the tilted ascending front‐to‐rear flow, the front overturning circulation, and the descending rear inflow of a squall line can be all determined from the cold pool buoyancy. The squall lines are therefore self‐organized dynamical systems that have limited degrees of freedom in their properties. The model advances the theoretical understanding of how mesoscale flow components interact to sustain organized convection. It provides a new tool to interpret squall line systems in high resolution models and to parameterize them in climate models. Key Points An analytical model is presented to obtain stream functions of steady‐state flows in squall lines along with far‐side solutions The model leads to prediction of squall line properties including propagation speed, cold pool depth, and mass fluxes Squall lines are organized nonlinear dynamical systems with small degrees of freedom under given environmental wind and convective available potential energy

Climate models exhibit significant rainfall bias in mountainous regions. One reason is the insufficient representation of orographic anisotropy in these models. In this study, we implement the orographic drag scheme with 3‐D orographic anisotropy (all flow directions (AFD)) into a general circulation model and investigate its impact on Nanling rainfall simulation where models have systematic dry bias in the summer. It is shown that the AFD alleviated the Nanling mountain rainfall bias by over 60%. This is through an anomalous “lower‐convergence‐higher‐divergence” deceleration pattern of the flow windward of the Nanling Mountains that enhanced vertical motion and low‐level moisture convergence. The results suggest the importance of explicit orographic anisotropy representation in rainfall simulation in mountainous regions. Plain Language Summary Simulation of the Nanling mountain summer rainfall has shown significant dry bias. In this study, we implemented a scheme considering fully impact of direction‐dependent change of orographic height on mountain flow in a global climate model to examine its effect on this bias. It is shown that the new scheme led to better simulation of the orographic rainfall compared to the original scheme due to improvement in moisture transport and vertical motion. This demonstrates that explicit representation of this effect for all flow directions is important in climate modeling especially in the simulation of the orographic rainfall. Hence, it should be necessary to represent this effect in the drag parameterization. Key Points Nanling mountain rainfall simulation is improved when an orographic drag scheme is used with anisotropy for all flow directions The impact is due to anomalous drag windward of the Mountains that enhanced vertical motion and low‐level moisture convergence The results suggest the importance of explicit orographic anisotropy in climate simulations

This study aimed to investigate risk factors associated with severe postoperative complications following hip fracture surgery in elderly patients and to develop a nomogram-based risk prediction model for these complications. A total of 627 elderly patients with hip fractures treated at Yongchuan Hospital of Chongqing Medical University from January 2015 to April 2024 were collected. 439 patients were assigned to the training cohort for model development, and 188 to the validation cohort for model assessment. The training cohort was stratified based on the presence or absence of severe complications. We employed LASSO regression, as well as univariate and multivariate logistic regression analyses, to identify significant factors. A nomogram was constructed based on the outcomes of the multivariate regression. The model's discriminative ability was assessed using the area under the receiver operating characteristic curve (AUC), while calibration plots and decision curve analysis (DCA) evaluated its calibration and stability. Internal validation was performed using the validation cohort. Out of the 627 patients, 118 (18.82%) experienced severe postoperative complications. Both LASSO regression and multivariate logistic analysis identified the modified 5-item frailty index (mFI-5) and the preoperative C-reactive protein to albumin ratio (CAR) as significant predictors of severe complications. The nomogram model, derived from the multivariate analysis, exhibited strong discriminative ability, with an AUC of 0.963 (95% CI: 0.946-0.980) for the training cohort and 0.963 (95% CI: 0.938-0.988) for the validation cohort. Calibration plots demonstrated excellent agreement between the nomogram's predictions and actual outcomes. Decision curve analysis (DCA) indicated that the model provided clinical utility across all patient scenarios. These findings were consistent in the validation cohort. Both the mFI-5 and CAR are predictive factors for severe postoperative complications in elderly patients undergoing hip fracture surgery.

The number of retracted scholarly articles has risen precipitously in recent years. Past surveys of the retracted literature each limited their scope to articles in PubMed, though many retracted articles are not indexed in PubMed. To understand the scope and characteristics of retracted articles across the full spectrum of scholarly disciplines, we surveyed 42 of the largest bibliographic databases for major scholarly fields and publisher websites to identify retracted articles. This study examines various trends among them. We found, 4,449 scholarly publications retracted from 1928-2011. Unlike Math, Physics, Engineering and Social Sciences, the percentages of retractions in Medicine, Life Science and Chemistry exceeded their percentages among Web of Science (WoS) records. Retractions due to alleged publishing misconduct (47%) outnumbered those due to alleged research misconduct (20%) or questionable data/interpretations (42%). This total exceeds 100% since multiple justifications were listed in some retraction notices. Retraction/WoS record ratios vary among author affiliation countries. Though widespread, only miniscule percentages of publications for individual years, countries, journals, or disciplines have been retracted. Fifteen prolific individuals accounted for more than half of all retractions due to alleged research misconduct, and strongly influenced all retraction characteristics. The number of articles retracted per year increased by a factor of 19.06 from 2001 to 2010, though excluding repeat offenders and adjusting for growth of the published literature decreases it to a factor of 11.36. Retracted articles occur across the full spectrum of scholarly disciplines. Most retracted articles do not contain flawed data; and the authors of most retracted articles have not been accused of research misconduct. Despite recent increases, the proportion of published scholarly literature affected by retraction remains very small. Articles and editorials discussing retractions, or their relation to research integrity, should always consider individual cases in these broad contexts. However, better mechanisms are still needed for raising researchers' awareness of the retracted literature in their field.

Underwater object detection plays a significant role in marine ecosystem research and marine species conservation. The improvement of related technologies holds practical significance. Although existing object-detection algorithms have achieved an excellent performance on land, they are not satisfactory in underwater scenarios due to two limitations: the underwater objects are often small, densely distributed, and prone to occlusion characteristics, and underwater embedded devices have limited storage and computational capabilities. In this paper, we propose a high-precision, lightweight underwater detector specifically optimizing for underwater scenarios based on the You Only Look Once Version 8 (YOLOv8) model. Firstly, we replace the Darknet-53 backbone of YOLOv8s with FasterNet-T0, reducing model parameters by 22.52%, FLOPS by 23.59%, and model size by 22.73%, achieving model lightweighting. Secondly, we add a Prediction Head for Small Objects, increase the number of channels for high-resolution feature map detection heads, and decrease the number of channels for low-resolution feature map detection heads. This results in a 1.2% improvement in small-object detection accuracy, while the remaining model parameters and memory consumption are nearly unchanged. Thirdly, we use Deformable ConvNets and Coordinate Attention in the neck part to enhance the accuracy in the detection of irregularly shaped and densely occluded small targets. This is achieved by learning convolution offsets from feature maps and emphasizing the regions of interest (RoIs). Our method achieves 52.12% AP on the underwater dataset UTDAC2020, with only 8.5 M parameters, 25.5 B FLOPS, and 17 MB model size. It surpasses the performance of large model YOLOv8l, at 51.69% AP, with 43.6 M parameters, 164.8 B FLOPS, and 84 MB model size. Furthermore, by increasing the input image resolution to 1280 × 1280 pixels, our model achieves 53.18% AP, making it the state-of-the-art (SOTA) model for the UTDAC2020 underwater dataset. Additionally, we achieve 84.4% mAP on the Pascal VOC dataset, with a substantial reduction in model parameters compared to previous, well-established detectors. The experimental results demonstrate that our proposed lightweight method retains effectiveness on underwater datasets and can be generalized to common datasets.

Climate models show a conspicuous summer warm and dry bias over the central United States. Using results from 19 climate models in the Coupled Model Intercomparison Project Phase 5 (CMIP5), we report a persistent dependence of warm bias on dry bias with the precipitation deficit leading the warm bias over this region. The precipitation deficit is associated with the widespread failure of models in capturing strong rainfall events in summer over the central U.S. A robust linear relationship between the projected warming and the present-day warm bias enables us to empirically correct future temperature projections. By the end of the 21st century under the RCP8.5 scenario, the corrections substantially narrow the intermodel spread of the projections and reduce the projected temperature by 2.5 K, resulting mainly from the removal of the warm bias. Instead of a sharp decrease, after this correction the projected precipitation is nearly neutral for all scenarios. Climate models repeatedly show a warm and dry bias over the central United States, but the origin of this bias remains unclear. Here the authors associate this bias to precipitation deficits in models and after applying a correction, projected precipitation in this region shows no significant changes.

Climate models simulate a wide range of temperatures in the Arctic. Here we investigate one of the main drivers of changes in surface temperature: the net surface heat flux in the models. We show that in the winter months of the dark Arctic, there is a more than two-fold difference in the net surface heat fluxes among the models, and this difference is dominated by the downward infrared radiation from clouds. Owing to the small amount of water vapor in the winter Arctic, infrared radiation from clouds transmits more easily to the surface in the Arctic than at other latitudes, resulting in large cloud radiative effect at the surface. The dominant role of the cloud effect is also found in the transient variability of the net surface heat flux. Results demonstrate that accurate simulation of clouds is crucial for determining the net surface heat flux, which in turn affects surface temperature and sea ice properties in the Arctic.