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Object detection has become faster and more precise due to improved computer vision systems. Many successful object detections have dramatically improved owing to the introduction of machine learning methods. This study incorporated cutting-edge methods for object detection to obtain high-quality results in a competitive timeframe comparable to human perception. Object-detecting systems often face poor performance issues. Therefore, this study proposed a comprehensive method to resolve the problem faced by the object detection method using six distinct machine learning approaches: stochastic gradient descent, logistic regression, random forest, decision trees, k-nearest neighbor, and naive Bayes. The system was trained using Common Objects in Context (COCO), the most challenging publicly available dataset. Notably, a yearly object detection challenge is held using COCO. The resulting technology is quick and precise, making it ideal for applications requiring an object detection accuracy of 97%. [ABSTRACT FROM AUTHOR]

Abstract The d-transition kagome metals provide a novel platform for exploring correlated superconducting state intertwined with charge ordering. However, the force of charge-density-wave (CDW) and superconductivity (SC) formation, and the mechanism underlying electron pairing remain elusive. Here, utilizing our newly developed methodology based on electride states as fingerprints, we propose a novel class of hydrogen-kagome superconductors AH3Li5 (A = C, Si, P) with ideal kagome band characteristics and elucidate the electron-phonon coupling (EPC) mechanism responsible for electron pairing. The representative compressed PH3Li5 and CH3Li5 demonstrates impressive superconducting transition temperatures (T cs) of 120.09 K and 57.18 K, respectively. Importantly, the CDW competes with SC thus resulting in a pressure-driven dome-shaped SC in CH3Li5, where the CDW order was induced by both EPC and Fermi surface nesting. Our study presents a scientific method for identifying high-T c hydrogen-kagome metals and provides new avenues to fundamentally understand the underlying mechanism of CDW and SC, thereby guiding future experimental investigations.

Abstract The thermal Hall effect recently provided intriguing probes to the ground state of exotic quantum matters. These observations of transverse thermal Hall signals lead to the debate on the fermionic versus bosonic origins of these phenomena. The recent report of quantum oscillations (QOs) in Kitaev spin liquid points to a possible resolution. The Landau level quantization would most likely capture only the fermionic thermal transport effect. However, the QOs in the thermal Hall effect are generally hard to detect. In this work, we report the observation of a large oscillatory thermal Hall effect of correlated Kagome metals. We detect a 180-degree phase change of the oscillation and demonstrate the phase flip as an essential feature for QOs in the thermal transport properties. More importantly, the QOs in the thermal Hall channel are more profound than those in the electrical Hall channel, which strongly violates the Wiedemann–Franz (WF) law for QOs. This result presents the oscillatory thermal Hall effect as a powerful probe to the correlated quantum materials.

Abstract Background High-flow nasal cannula (HFNC) has emerged as a promising noninvasive method for delivering oxygen to critically ill patients, particularly those with sepsis and acute lung injury. However, uncertainties persist regarding its therapeutic benefits in this specific patient population. Methods This retrospective study utilized a propensity score-matched cohort from the Medical Information Mart in Intensive Care-IV (MIMIC-IV) database to explore the correlation between HFNC utilization and mortality in patients with sepsis-induced acute lung injury. The primary outcome was 28-day all-cause mortality. Results In the propensity score-matched cohort, the 28-day all-cause mortality rate was 18.63% (95 out of 510) in the HFNC use group, compared to 31.18% (159 out of 510) in the non-HFNC group. The use of HFNC was associated with a lower 28-day all-cause mortality rate (hazard ratio [HR] = 0.53; 95% confidence interval [CI] = 0.41–0.69; P

Abstract In the recently discovered kagome metal CsV3Sb5, an intriguing proposal invoking a doped Chern insulator state suggests the presence of small Chern Fermi pockets hosting spontaneous orbital-currents and large orbital magnetic moments. While the net thermodynamic magnetization is nearly insensitive to these moments, due to their antiferromagnetic alignment, their presence can be revealed by the Zeeman effect, which shifts electron energies in magnetic fields with a proportionality given by the effective g−factor. Here, we determine the g-factor using the spin-zero effect in magnetic quantum oscillations. A large g-factor enhancement is visible only in magnetic breakdown orbits between conventional and concentrated Berry curvature Fermi pockets that host large orbital moments. Such Berry-curvature-generated large orbital moments are almost always concealed by other effects. In this system, however, magnetic breakdown orbits due to the proximity to a conventional Fermi-surface section allow them to be visibly manifested in magnetic quantum oscillations. Our results provide a remarkable example of the interplay between electronic correlations and more conventional electronic bands in quantum materials.

Eating behaviors are associated with mental health. However, although most studies have assessed specific eating behaviors, none have evaluated the association of eating rates and post-dinner snacking with depressive symptoms. This study investigated this relationship in adult Chinese men. A total of 1552 participants aged 25–68 years were included in this study. Logistic regression analysis was conducted to examine the relationship between eating behaviors and depressive symptoms, and the corresponding odds ratios and 95% confidence intervals (CIs) were calculated. Self-administered questionnaires were used to evaluate eating rates and post-dinner snacking, and the Self-Rating Depression Scale was used to assess depressive symptoms. In the unadjusted model, the odds ratios and 95% CIs for depressive symptoms associated with normal and fast eating compared with slow eating were 1.47 (1.15, 1.89) and 1.60 (1.14, 2.23), respectively (p for trend = 0.002). This significant positive association remained unchanged in the final adjusted model (p for trend = 0.006). No significant relationship was found between post-dinner snacking and depressive symptoms. These findings showed that eating fast was linked to a higher risk of depressive symptoms and suggested that speed of eating may influence the mental health of adult men.

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.

FeGe is a kagome material that exhibits both charge density wave (CDW) order and magnetic order. The CDW order is developed deep inside the A-type antiferromagnetic phase in FeGe, providing a unique platform to investigate the interplay between CDW and magnetism. However, the driving mechanism of the CDW phase remains controversial. In this work, we performed high-pressure electrical transport and x-ray diffraction measurements combined with density-functional theory calculations to investigate the evolution of the CDW of FeGe under pressure. In contrast to conventional CDW materials, the CDW transition temperature of FeGe increases with increasing pressure, indicating the unconventional mechanism of CDW in this material. More interestingly, another possible CDW with a sqrt[3]×sqrt[3]×6 superlattice emerges above 20 GPa, which may be explained by the calculated metastable CDW states under high pressure. These observations exclude the possibility of Van Hove singularities nesting as a CDW driving force. Our results unveil versatile CDW states and broaden the study of intertwined electronic states in the magnetic kagome metal FeGe.

At present, rice potted seedling transplanting machine is mostly suitable for high-speed, large seedlings and large plant distance rice transplanting. In order to adapt to the transplanting of different types of rice potted seedlings, this study carried out theoretical research and institutional design for transplanting rice potted seedlings suitable for small seedlings and small plant spacing. A 2R open-chain rice potted seedling transplanting mechanism with two non-circular gears meshing to achieve the ideal transplanting posture was developed, and an optimization software for potted seedling transplanting was developed by establishing the kinematic model of the transplanting mechanism, which simulated the motion trajectory and attitude of the transplanting arm during the transplanting process. Through the optimization of the optimization design software parameters, a set of parameters that meet the requirements of transplanting rice potted seedlings in the rear insert rice potted are obtained, a transplanting mechanism suitable for walking rice potted seedling transplanting machine is designed and developed, and the experiments of rice potted seedling picking, conveying and planting are completed. The success rate of the designed walk-type rice potted seedling transplanting mechanism on the bench was 96.7%, and the qualification rate of the whole machine transplanting test was 89.1%.

A knotter is a core component for the automatic bundling of agricultural materials, and a knotter with double-fluted discs is one type. Currently, the research on knotters with double-fluted discs has gradually transitioned from structural design to reliability optimization. To address rope-clamping failures in the rope-clamping drive mechanisms in knotters, the specific failure position of the rope-clamping mechanism and the failure causes were analyzed first. The redesign of the rope-clamping drive mechanism in knotters with double synclastic fluted discs was proposed, including structure optimization and 3D modeling using the GearTrax/KISSsoft and SolidWorks software. A virtual prototype model of a knotter with a flexible rope was established by combining ANSYS with the ADAMS software. A rigid–flexible coupling dynamic simulation of the knotter was carried out using ADAMS, and the simulation results were used as the data input for the ANSYS nCode DesignLife module for the fatigue life simulation of the weak parts (the worm shaft) of the knotter. The operation test results for the rope-clamping drive mechanism indicate that the redesigned rope-clamping drive mechanism is reliable in transmission, with a rope-clamping success rate of 100%. The actual operation times for the worm shaft exceed the minimum fatigue life obtained through joint simulation. The applied joint simulation method has high simulation accuracy.

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.

Abstract The insulator-metal transition in Mott insulators, known as the Mott transition, is usually accompanied with various novel quantum phenomena, such as unconventional superconductivity, non-Fermi liquid behavior and colossal magnetoresistance. Here, based on high-pressure electrical transport and XRD measurements, and first-principles calculations, we find that a unique pressure-induced Mott transition from an antiferromagnetic Mott insulator to a ferromagnetic Weyl metal in the iron oxychalcogenide La2O3Fe2Se2 occurs around 37 GPa without structural phase transition. Our theoretical calculations reveal that such an insulator-metal transition is mainly due to the enlarged bandwidth and diminishing of electron correlation at high pressure, fitting well with the experimental data. Moreover, the high-pressure ferromagnetic Weyl metallic phase possesses attractive electronic band structures with six pairs of Weyl points close to the Fermi level, and its topological property can be easily manipulated by the magnetic field. The emergence of Weyl fermions in La2O3Fe2Se2 at high pressure may bridge the gap between nontrivial band topology and Mott insulating states. Our findings not only realize ferromagnetic Weyl fermions associated with the Mott transition, but also suggest pressure as an effective controlling parameter to tune the emergent phenomena in correlated electron systems.

Abstract Full‐Heusler alloys (fHAs) exhibit high mechanical strength with earth‐abundant elements, but their metallic properties tend to display small electron diffusion thermopower, limiting potential applications as excellent thermoelectric (TE) materials. Here, it is demonstrated that the Co‐based fHAs Co2XAl (X = Ti, V, Nb) exhibit relatively high thermoelectric performance due to spin and charge coupling. Thermopower contributions from different magnetic mechanisms, including spin fluctuation and magnon drag are extracted. A significant contribution to thermopower from magnetism compared to that from electron diffusion is demonstrated. In Co2TiAl, the contribution to thermopower from spin fluctuation is higher than that from electron diffusion, resulting in an increment of 280 µW m−1 K−2 in the power factor value. Interestingly, the thermopower contribution from magnon drag can reach up to ‐47 µV K−1, which is over 2400% larger than the electron diffusion thermopower. The power factor of Co2TiAl can reach 4000 µW m−1 K−2 which is comparable to that of conventional semiconducting TE materials. Moreover, the corresponding figure of merit zT can reach ≈0.1 at room temperature, which is significantly larger than that of traditional metallic materials. The work shows a promising unconventional way to create and optimize TE materials by introducing magnetism.

The climate significance of stalagmite δ18O values from the East Asian monsoon region are still under much debated during the past years. The bone of contention focuses on whether the stalagmite δ18O can reflect the summer monsoon intensity or summer monsoon rainfall, either reflecting the vapor source variation. To better understand the significance of stalagmite δ18O in this region, This study analyses the relationship between stalagmite δ18O, summer monsoon rainfall and El Niño-Southern oscillation (ENSO) activity from the interannual-decadal timescale to the millennial-orbital timescale. This work is based on the anti-phase relationship between North China rainfall and the middle and lower reaches of the Yangtze River and the East Asia/Pacific (EAP) teleconnection. The results show that the more depleted stalagmite δ18O corresponds to higher rainfall in North China and lower rainfall in the Meiyu area, and more enriched stalagmite δ18O corresponds to lower rainfall in North China and higher rainfall in the Meiyu area. This relationship is found not only on interannual to decadal timescales but also on millennial to orbital timescales. Therefore, the stalagmite δ18O could reflect the summer monsoon intensity and shows a consistent relationship with rainfall in East China. We also found that stalagmite δ18O influenced by ENSO only occurred in the rainfall regime in East China, and the effect on stalagmite δ18O by the convective activity in the source area induced by ENSO activity was neglected. For example, in the La Niña state, the convective activity in the South China Sea and the Philippine Sea is enhanced, the position of the West Pacific subtropical high (WPSH) is farther north, the Meiyu rainfall in the middle and lower reaches of theYangtze River is shortened, the summer rainfall in North China is prolonged, and stalagmite δ18O is depleted. In the El Niño state, the convective activity in the South China Sea and the Philippine Sea is depressed, the position of the West Pacific subtropical high (WPSH) is farther south, the Meiyu rainfall in the middle and lower reaches of the Yangtze River is prolonged, and the summer rainfall in North China is shortened, and stalagmite δ18O is enriched. In addition, the vapor source analysis indicates that the vapor transport from the Philippine Sea has a minor influence on the stalagmite δ18O in the East Asian summer monsoon region. Finally, we conclude that the stalagmite δ18O in the East Asian summer monsoon region is an East Asian summer monsoon intensity proxy.

The hydrogeochemistry of cave drip water is an important environmental index in cave systems, and drip water monitoring may be an essential solution for paleoclimate reconstructions. We measured the hydrogeochemical properties of the seasonal and perennial drip water and CO2 concentrations from 2015 to 2019 in the Liangfeng Cave, Guilin, Southwest China. This study identified the difference in the regional environmental records in the perennial and seasonal drip water. By comparing the regional climate data recorded by the drip water, the results showed that the perennial drip water recorded regional climate information throughout the year, whereas the seasonal drip water only recorded the high precipitation periods. The precipitation during the 2015 dry season was abnormally high, and higher than the values in other rainy seasons. This indicates that hydrogeochemistry only represents changes in precipitation and not the alternation of dry and rainy seasons during this period. HIGHLIGHTS The first long-term monitoring study was performed in well ventilation Cave, Guilin, Southwest China.; The purpose of this study was to interpret the precipitation and drought event signals recorded in hydrogeochemical properties of the cave water.; The seasonal variation hydrogeochemical responses of the cave drip water can record regional climate information.;

Kagome lattices composed of transition-metal ions have recently attracted great interest. Here, the authors report a new class of vanadium-based compounds with kagome bilayers which show lines of Dirac nodes in reciprocal space and superconductivity under pressure.

Titanium nitride (TiN) thin films are used for the fabrication of superconducting devices due to their chemical stability against oxidization and high quality at interfaces. The high-pressure technique serves as a useful tool to understand the mechanical and electrical properties of materials, which is crucial for practical applications. However, high-pressure transport measurements of thin films are extremely difficult due to the limited sample space of high-pressure cells and the fragility of thin films. Here, we successfully carried out high-pressure electrical transport and Raman measurements on TiN films up to ∼50 GPa. The superconducting transition temperature gradually decreases with increasing pressure, which can be attributed to the decrease of electron -phonon coupling and is consistent with our first-principles calculations. In addition, the coexistence of a symmetry-enforced Dirac nodal chain and a nodal box is revealed by our calculations in TiN. Our work provides a promising way to study the physical properties of thin films at high pressure, which would broaden the high-pressure research field.

Transplanting rice pot seedlings without damaging the roots, which promotes early tillering, is an effective measure to enhance rice yield and quality. This study aimed to obtain the mechanized-transplanting trajectory and attitude of rice pot seedlings by utilizing non-circular planetary-gear trains, focusing on the three key actions of rice pot-seedling transplanting: seedling picking, conveying, and planting. A lightweight and simplified rice pot-seedling transplanting machinery was designed, referring to the motion characteristics of artificially transplanting rice pot seedlings by first pulling them out and then planting them. Key technologies such as non-circular gear trains, the rice seedling supply system, the transmission system, and the rice seedling-picking device were studied, and their key components were designed and manufactured, resulting in the creation of two physical model machines: an ordinary ride type and a high-speed type. The seedling-picking test and field-transplanting test showed that the rice pot-seedling transplanting mechanism can accomplish the rice pot-seedling picking, rice conveying, and planting actions. The designed operation efficiency yielded a planting-depth qualification rate of over 92%, a seedling injury rate of less than 1.2%, and a missed-transplanting rate of less than 2%.

To better meet the requirements of mechanized transplanting of pepper plug seedlings, this study explores the seedling picking mechanism of a fully automatic pepper transplanting machine. It introduces a novel “eagle beak” type trajectory for seedling picking and designs a probe-type mechanism for pepper plug seedling retrieval. We establish a kinematic theoretical model and delineate the composition and operational principles of this probe-type mechanism. Additionally, we develop an auxiliary optimization software tailored based on Visual Basic 6.0 visual programming software for this mechanism. It employs a blend of manual fine-tuning and a “parameter guidance” optimization algorithm, enabling the determination of 11 optimal target parameters. Our comparative analysis between the theoretical model, optimization software, and high-speed camera experiments reveals a strong correlation in the motion trajectories, and the maximum error of the pose angle is 1.2°. To validate the mechanism’s design, we conducted a seedling retrieval experiment. In this test, the success rates of the seedling harvesting mechanism at speeds of 30, 40, and 50 r/min were 96.4%, 94.3%, and 91.4%, respectively, thus demonstrating its practical feasibility.

Aiming to solve the problem of knot-tripping failure caused by severe wear between the spherical roller and planar cam of the knotter, this paper first establishes a calculation model of the spatial cam contour surface. The knot-tripping mechanism in the knotter is designed as a line-contact curved-surface cam mechanism, with the cutter arm swinging in accordance with sinusoidal acceleration. The design significantly reduces the contact stress between the cam and the roller, compared to the original knot-tripping mechanism. Additionally, it eliminates the impact between the spherical roller and the planar cam. Based on the Archard model, the calculation model for cam-roller wear in the knot-tripping mechanism has been derived and utilized for wear calculation. The wear test results of the knot-tripping mechanism with an aluminum cam show that the curved cam has a wear amount that is 43%, 56%, 46%, and 37% lower than that of the planar cam after tying the knot 200 times, 600 times, 1300 times, and 2000 times, respectively. Under the condition that the twine tension is set to 120 N, and the rotation speed of the fluted disc is 60 rpm, the deviations between the calculated value and the measured value of the wear amount of the curved cam are 9.48%, 6.01%, 7.27%, and 9.95%, respectively. This validates the accuracy of the spatial cam wear model and the correctness of the curved cam design.

Studies agree on a significant global mean sea level rise in the 20th century and its recent 21st century acceleration in the satellite record. At regional scale, the evolution of sea level probability distributions is often assumed to be dominated by changes in the mean. However, a quantification of changes in distributional shapes in a changing climate is currently missing. To this end, we propose a novel framework quantifying significant changes in probability distributions from time series data. The framework first quantifies linear trends in quantiles through quantile regression. Quantile slopes are then projected onto a set of four orthogonal polynomials quantifying how such changes can be explained by independent shifts in the first four statistical moments. The framework proposed is theoretically founded, general, and can be applied to any climate observable with close-to-linear changes in distributions. We focus on observations and a coupled climate model (GFDL-CM4). In the historical period, trends in coastal daily sea level have been driven mainly by changes in the mean and can therefore be explained by a shift of the distribution with no change in shape. In the modeled world, robust changes in higher order moments emerge with increasing $ {\mathrm{CO}}_2 $ concentration. Such changes are driven in part by ocean circulation alone and get amplified by sea level pressure fluctuations, with possible consequences for sea level extremes attribution studies.

The field harvesting process of harvesting machinery is often affected by high workload and environmental factors that can impede/delay manual rowing, thereby leading to lower efficiency and quality in the residual film collector. To address this challenge, an automatic rowing control system using the 4mz-220d self-propelled residual film collector as the experimental carrier was proposed in this study. Cotton stalks in the ridges were chosen as the research object, and a comprehensive application of key technologies, machinery, and electronic control was used, thereby incorporating a pure tracking model as the path-tracking control method. To achieve the automatic rowing function during the field traveling process, the fuzzy control principle was implemented to adjust the forward distance within the pure tracking model dynamically, and the expected steering angle of the steering wheel was determined based on the kinematic model of the recovery machine. The MATLAB/Simulink software was utilized to simulate and analyze the proposed model, thus achieving significant improvements in the automation level of the residual film collector. The field harvesting tests showed that the average deviation of the manual rowing was 0.144 m, while the average deviation of the automatic rowing was 0.066 m. Moreover, the average lateral deviation of the automatic rowing was reduced by 0.078 m with a probability of deviation within 0.1 m of 95.71%. The research study demonstrated that the designed automatic rowing system exhibited high stability and robustness, thereby meeting the requirements of the autonomous rowing operations of residual film collectors. The results of this study can serve as a reference for future research on autonomous navigation technology in agriculture.