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Long‐term projections of global‐ocean thermal expansion (GTE) and the dynamic sea level (DSL) change are analyzed with 34 new CMIP5 models and under three greenhouse‐gas emission scenarios. Multi‐model ensemble mean (MEM) and ensemble standard deviation are calculated to identify robust features and quantify uncertainty. While the MEM of GTE shows moderate difference by 2100, with magnitudes of 13, 18 and 28 cm in RCP2.6, RCP4.5 and RCP8.5, respectively, it increases and diverges significantly by 2300, with magnitudes of 21, 52 and 119 cm in the three scenarios. Model‐to‐model spread seems reduced in CMIP5 compared to CMIP3. The MEM changes of the DSL show similar patterns between different RCPs, but with progressively larger magnitudes in RCP2.6, RCP4.5 and RCP8.5. Notable features identified previously in the CMIP3 projections also occur in CMIP5, indicating their robustness across generations of climate model and emission scenario. The CMIP5 models still show disagreement in projecting the DSL changes, even under the same external forcing. Key Points Thermal expansion and the dynamic sea level change are highly scenario‐dependent The CMIP5 results are generally consistent with those of CMIP3 Models still show uncertainty in the dynamic sea level projection

Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing resource utilization, managing crop diseases and pesticides, and reducing environmental impact are discussed. The introduction comprehensively covers LiDAR technology in precision agriculture, detailing airborne, terrestrial, and mobile systems along with their specialized applications in the field. After that, the paper reviews the several uses of LiDAR in agricultural cultivation, including crop growth and yield estimate, disease detection, weed control, and plant health evaluation. The use of LiDAR for soil analysis and management, including soil mapping and categorization and the measurement of moisture content and nutrient levels, is reviewed. Additionally, the article examines how LiDAR is used for harvesting crops, including its use in autonomous harvesting systems, post-harvest quality evaluation, and the prediction of crop maturity and yield. Future perspectives, emergent trends, and innovative developments in LiDAR technology for precision agriculture are discussed, along with the critical challenges and research gaps that must be filled. The review concludes by emphasizing potential solutions and future directions for maximizing LiDAR’s potential in precision agriculture. This in-depth review of the uses of LiDAR gives helpful insights for academics, practitioners, and stakeholders interested in using this technology for effective and environmentally friendly crop management, which will eventually contribute to the development of precision agricultural methods.

Sea level rise (SLR) shows important spatiotemporal variability. A better understanding of characteristics and mechanisms of the variability is critical for future SLR projection and coastal preparedness. Here we analyze various observational and modeling data of sea level and its components, atmospheric pressure and winds, and ocean circulation in the North Atlantic. Both the century-long tide gauge data and the more recent altimetry data reveal a rapid decadal acceleration of SLR during 2010–22 along the U.S. East Coast and the Gulf of Mexico coast. The acceleration is most notable on the Southeast and Gulf Coasts, as quantified by the decadal rise rate, extreme annual sea level departure from the longterm trend, as well as the sea level record-breaking frequency and magnitude. Our analysis suggests that this SLR acceleration is largely a lagged response to the observed slowdown of the Atlantic meridional overturning circulation in 2009–10. In the North Atlantic, the response is characterized by a large-scale pattern of contrast changes in dynamic sea level between the Eastern Subpolar Gyre and the U.S. Southeast and Gulf Coasts. The latest global climate model generally captures this observed pattern and projects that further increase in greenhouse gas forcing will modify it over the twenty-first century. The faster SLR on the Southeast and Gulf Coasts, at a rate of more than 10 mm yr−1 during 2010–22, coincided with active and even record-breaking North Atlantic hurricane seasons in recent years. As a consequence, the elevated storm surge exacerbated coastal flooding and damage particularly on the Gulf Coast.

Sleep duration and quality have several effects on human health. Some previous studies have shown an association between sleep duration and quality and mental health, but the results of those studies were inconsistent. Additionally, studies on sleep duration and its impact on depressive symptoms specifically among young Chinese adults are limited. Hence, this study aimed to investigate the association between duration and quality of sleep and depressive symptoms among Chinese university students. We designed a cross-sectional study comprising 9,515 Chinese university students. Sleep duration and quality were assessed using a self-reported questionnaire. Depressive symptoms were assessed based on the Self-rating Depression Scale score. Logistic regression models were used to analyze the association between sleep duration and quality and depressive symptoms. The results showed that good sleep quality was associated with a lower prevalence of depressive symptoms. In the final adjusted model, the odds ratios and 95% confidence intervals for the prevalence of depressive symptoms in those with poor sleep quality compared with those with normal and good sleep quality were 0.88 (0.77, 1.01) and 0.82 (0.81, 0.96), respectively (p for trend = 0.014). Moreover, short sleep duration was associated with a higher prevalence of depressive symptoms based on the crude model and final adjusted model (p for trend = 0.048 and 0.042, respectively). Poor sleep quality and short sleep duration were associated with a higher prevalence of depressive symptoms in this study population. These results suggest that reduced sleep duration and quality may be risk factors for mental health disorders among university students.

Due to its large northward heat transport, the Atlantic meridional overturning circulation influences both weather and climate at the mid-latitude Northern Hemisphere. Here we use a state-of-the-art global weather/climate modeling system with high resolution (GFDL CM4C192) to quantify this influence focusing on the U.S. extreme cold weather during winter. We perform a control simulation and the water-hosing experiment to obtain two climate states with and without a vigorous Atlantic meridional overturning circulation. We find that in the control simulation with an overturning circulation, the U.S. east of the Rockies is a region characterized by intense north-south heat exchange in the atmosphere during winter. Without the northward heat transport by the overturning circulation in the hosing experiment, this channel of atmospheric heat exchange becomes even more active through the Bjerknes compensation mechanism. Over the U.S., extreme cold weather intensifies disproportionately compared with the mean climate response after the shutdown of the overturning circulation. Our results suggest that an active overturning circulation in the present-day climate likely makes the U.S. winter less harsh and extreme.

Bale density is one of the main performance indicators to measure the quality of baler operation. In this study, a real-time baler bale density monitoring system was designed for the problem of difficult real-time measurement of bale density on round balers. Firstly, a weighing calculation model for the rolling and sliding stage of the bale was established, and the dynamic characteristics during the contact between the bale and the inclined surface were analyzed based on ADAMS dynamics simulation. Then, a real-time monitoring system for the bale density based on the contact pressure of the inclined surface, attitude angle measurement and hydraulic monitoring of the cylinder was constructed, and the accuracy of the weighing model was confirmed. The system was used to observe and analyze the changes in the pitch angle of the carrier table and the oil pressure in the rod chamber of the backpack cylinder during the operation of the round baler. Finally, the monitoring system was calibrated and the dynamic calibration equations were obtained. The results show that the maximum error between the calculated value of the original weighing model and the actual weight was 3.63%, the maximum error of the calculated value of the weighing model corrected by the calibration equations was 3.40% and the measurement accuracy could be satisfied. The results show that the system was highly accurate and met the practical needs of bale weighing in the field.

The authors characterize impacts on heat in the ocean climate system from transient ocean mesoscale eddies. Their tool is a suite of centennial-scale 1990 radiatively forced numerical climate simulations from three GFDL coupled models comprising the Climate Model, version 2.0–Ocean (CM2-O), model suite. CM2-O models differ in their ocean resolution: CM2.6 uses a 0.1° ocean grid, CM2.5 uses an intermediate grid with 0.25° spacing, and CM2-1deg uses a nominal 1.0° grid. Analysis of the ocean heat budget reveals that mesoscale eddies act to transport heat upward in a manner that partially compensates (or offsets) for the downward heat transport from the time-mean currents. Stronger vertical eddy heat transport in CM2.6 relative to CM2.5 accounts for the significantly smaller temperature drift in CM2.6. The mesoscale eddy parameterization used in CM2-1deg also imparts an upward heat transport, yet it differs systematically from that found in CM2.6. This analysis points to the fundamental role that ocean mesoscale features play in transient ocean heat uptake. In general, the more accurate simulation found in CM2.6 provides an argument for either including a rich representation of the ocean mesoscale in model simulations of the mean and transient climate or for employing parameterizations that faithfully reflect the role of eddies in both lateral and vertical heat transport.

Intelligent pest detection algorithms are capable of effectively detecting and recognizing agricultural pests, providing important recommendations for field pest control. However, existing recognition models have shortcomings such as poor accuracy or a large number of parameters. Therefore, this study proposes a lightweight and accurate rice pest detection algorithm based on improved YOLOv8. Firstly, a Multi-branch Convolutional Block Attention Module (M-CBAM) is constructed in the YOLOv8 network to enhance the feature extraction capability for pest targets, yielding better detection results. Secondly, the Minimum Points Distance Intersection over Union (MPDIoU) is introduced as a bounding box loss metric, enabling faster model convergence and improved detection results. Lastly, lightweight Ghost convolutional modules are utilized to significantly reduce model parameters while maintaining optimal detection performance. The experimental results demonstrate that the proposed method outperforms other detection models, with improvements observed in all evaluation metrics compared to the baseline model. On the test set, this method achieves a detection average precision of 95.8% and an F1-score of 94.6%, with a model parameter of 2.15 M, meeting the requirements of both accuracy and lightweightness. The efficacy of this approach is validated by the experimental findings, which provide specific solutions and technical references for intelligent pest detection.

Agriculture is currently transitioning toward higher intelligence and facility-based production, where harvesting robots play a crucial role in enhancing efficiency and ensuring standardized output. Addressing the challenges of inaccurate picking pose estimation and limited reachability in greenhouse environments, this paper proposes a reachable grasping pose estimation method based on Particle Swarm Optimization (PSO). First, initial poses are calculated via instance segmentation and keypoint extraction. Subsequently, a fitness function is constructed based on inverse kinematics, and the PSO algorithm is employed to iteratively search for optimal reachable poses. To further tackle planning difficulties in confined spaces, a two-stage path planning method based on cost maps is introduced. A series of performance metrics were designed to validate the proposed pose estimation and path planning methods through simulation experiments. In real-world field tests, the system achieved a harvesting success rate of 85%, significantly outperforming existing methods. The results demonstrate that the proposed approach substantially enhances the operational feasibility and success rate of tomato harvesting robots.

During the winter of 2013–2014, the averaged tide gauge (TG) coastal sea level (CSL) anomaly north of 40°N was a record low of −107 mm for the period of 1948–2016. Statistical analysis indicates that this large drop was a once-in-a-century event and closely related to an unusual ocean warming event known as “The Blob”. The Blob developed in the NE Pacific during the winter of 2013–2014. Both the Blob and record-low CSL can be attributed to wind changes associated with an unusually high sea level pressure (SLP) pattern over the NE Pacific. The anomalous local longshore winds induced by the positive SLP anomalies caused strong offshore Ekman transport along the coast of NE Pacific, thereby leading to the record-low CSL. In addition, the steric sea level changes also contributed a significant part (17%) to the record-low CSL. The Pacific Decadal Oscillation (PDO), as the primary variability mode in the NE Pacific on decadal time scales, did not contribute to the emergence of this extreme CSL event.