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To improve the automation level and operation quality of China's beet harvester and reduce the loss due to damaged and missed excavation, this study used a self-developed sugar beet combine harvester and field simulation experiment platform, based on the single-factor bench test of the automatic row following system in the early stage, taking hydraulic flow A, spring preload B, and forward speed C which have significant influence on performance indices as test factors, and taking the missed excavation rate, breakage rate and reaction time as performance indices, the orthogonal experimental study on the parameter optimization of the three-factor and three-level automatic row following system with the first-order interaction of various factors was carried out. The results of the orthogonal experiments were analyzed using range analysis and variance analysis. The results showed that there were differences in the influence degree, factor priority order and first-order interaction, and the optimal parameter combination on each performance index. A weighted comprehensive scoring method was used to optimize and analyze each index. The optimal parameter combination of the overall operating performance of the automatic row following system was A2B2C1, that is, the hydraulic flow was 25 L/min, the forward speed was 0.8 m/s, and the spring preload was 198 N. Under this combination, the response time was 0.496 s, the missed excavation rate was 2.35%, the breakage rate was 3.65%, and the operation quality was relatively good, which can meet the harvest requirements. The comprehensive optimization results were verified by field experiments with different ridge shapes and different planting patterns. The results showed that the mean values of the missed excavation rate of different planting patterns of conventional straight ridges and extremely large "S" ridges were 2.23% and 2.69%, respectively, and the maximum values were 2.39% and 2.98%, respectively; the average damage rates were 3.38% and 4.14%, and the maximum values were 3.58% and 4.48%, which meet the industry standards of sugar beet harvester operation quality. The overall adaptability of the automatic row following system is good. This study can provide a reference for research on automatic row following harvesting systems of sugar beets and other subsoil crop harvesters. [ABSTRACT FROM AUTHOR]

A row-following system based on end-to-end learning for an agricultural robot in an apple orchard was developed in this study. Instead of dividing the navigation into multiple traditional subtasks, the designed end-to-end learning method maps images from the camera directly to driving commands, which reduces the complexity of the navigation system. A sample collection method for network training was also proposed, by which the robot could automatically drive and collect data without an operator or remote control. No hand labeling of training samples is required. To improve the network generalization, methods such as batch normalization, dropout, data augmentation, and 10-fold cross-validation were adopted. In addition, internal representations of the network were analyzed, and row-following tests were carried out. Test results showed that the visual navigation system based on end-to-end learning could guide the robot by adjusting its posture according to different scenarios and successfully passing through the tree rows. [ABSTRACT FROM AUTHOR]

Detection navigation baseline is primary for the automation of maize weeder in seedling. In the navigation technology based on machine vision, maize seeding or weed near the camera is photographed as a discrete area, while a plant far away from the camera is photographed as a strip area along with other plants in the same row. The two problems cannot be solved by one method. However, in this paper, an algorithm of detection navigation baseline in the row-following operation of maize weeder based on axis extraction was proposed to solve the both problems. Firstly, plants are distinguished from the background based on color feature, and the binary image is acquired. Secondly, plants are described as a set of connected components with numbers after connected components labeling and noise clearing. Thirdly, the axes of all connected components are extracted according to the calculation method of rotary inertia in physics. Next, the abnormal connected components with axes are deleted because the angles between the axes and X-axis are above angle threshold. Then, the judgment model is built based on angle tolerance and distance tolerance, the connected components in a same row based on this model through two-step traversal are merged, and a new axis is re-extracted as the axis of the plant row. Finally, the navigation baselines are detected based on the axes of the plant row. The experimental results show that the accuracy of this algorithm is more than 93%, and the computing time is less than 1.6 s, which can meet the accuracy and real-time performance requirements of maize weeder. [ABSTRACT FROM AUTHOR]

Agricultural robotics is a complex, challenging, and exciting research topic nowadays. However, orchard environments present harsh conditions for robotics operability, such as terrain irregularities, illumination, and inaccuracies in GPS signals. To overcome these challenges, reliable landmarks must be extracted from the environment. This study addresses the challenge of accurate, low-cost, and efficient landmark identification in orchards to enable robot row-following. First, deep learning, integrated with depth information, is used for real-time trunk detection and location. The in-house dataset used to train the models includes a total of 2453 manually annotated trunks. The results show that the trunk detection achieves an overall mAP of 81.6%, an inference time of 60 ms, and a location accuracy error of 9 mm at 2.8 m. Secondly, the environmental features obtained in the first step are fed into the DWA. The DWA performs reactive obstacle avoidance while attempting to reach the row-end destination. The final solution considers the limitations of the robot’s kinematics and dynamics, enabling it to maintain the row path and avoid obstacles. Simulations and field tests demonstrated that even with a certain initial deviation, the robot could automatically adjust its position and drive through the rows in the real orchard.

Intra-row weeding is a time consuming and challenging task. Therefore, a rotary weeder implement for an autonomous electrical robot was developed. It can be used to remove the weeds of the intra-row area of orchards and vineyards. The hydraulic motor of the conventional tool was replaced by an electric motor and some mechanical parts were refabricated to reduce the overall weight. The side shift, the height and the tilt adjustment were performed by linear electric motors. For detecting the trunk positions, two different methods were evaluated: A conventional electromechanical sensor (feeler) and a sonar sensor. The robot performed autonomous row following based on two dimensional laser scanner data. The robot prototype was evaluated at a forward speed of 0.16 ms−1 and a working depth of 40 mm. The overall performance of the two different trunk detection methods was tested and evaluated for quality and power consumption. The results indicated that an automated intra-row weeding robot could be an alternative solution to actual machinery. The overall performance of the sonar was better than the adjusted feeler in the performed tests. The combination of autonomous navigation and weeding could increase the weeding quality and decrease power consumption in future.

Agricultural robotics is a complex, challenging, and exciting research topic nowadays. However, orchard environments present harsh conditions for robotics operability, such as terrain irregularities, illumination, and inaccuracies in GPS signals. To overcome these challenges, reliable landmarks must be extracted from the environment. This study addresses the challenge of accurate, low-cost, and efficient landmark identification in orchards to enable robot row-following. First, deep learning, integrated with depth information, is used for real-time trunk detection and location. The in-house dataset used to train the models includes a total of 2453 manually annotated trunks. The results show that the trunk detection achieves an overall mAP of 81.6%, an inference time of 60 ms, and a location accuracy error of 9 mm at 2.8 m. Secondly, the environmental features obtained in the first step are fed into the DWA. The DWA performs reactive obstacle avoidance while attempting to reach the row-end destination. The final solution considers the limitations of the robot's kinematics and dynamics, enabling it to maintain the row path and avoid obstacles. Simulations and field tests demonstrated that even with a certain initial deviation, the robot could automatically adjust its position and drive through the rows in the real orchard. [ABSTRACT FROM AUTHOR]

Copyright of Arboriculture & Urban Forestry is the property of International Society of Arboriculture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study focuses on motion planning and reference trajectory tracking control of an autonomous agricultural vehicle to achieve precise row following and turning. The smooth time-varying feedback control method was adapted to the system to generate the required control commands. The mathematical representations for motion planning and controllers were constructed based on the car-like robot model. An algorithm to detect trees and rows of trees of an orchard was developed using the Hough transform approach. A new type of turning procedure, called knot-like turning, was proposed to perform turning from one row to another. A simulation environment was created to test and analyze the developed system. To obtain the real data from a field, the trees and rows of trees of a cherry orchard were scanned using a laser scanner rangefinder sensor. Then, the scanned data were moved to the simulation environment to generate the desired trajectory, which was followed by an autonomous agricultural vehicle. The simulation environment made it possible to determine the performance of the proposed motion planning, reference trajectory generation, tracking control and turning procedures. The results presented here indicate that the proposed methodology could be used for desired trajectory tracking tasks for agricultural operations in the case that minimum tracking errors in both straight and turning motions are needed. [ABSTRACT FROM AUTHOR]

Selective sweep is an important phenomenon in the aspect of natural selection. It plays significant role in adaptability as well as survival of species, including crop cultivars. Various existing approaches for selective sweep analysis are mostly built on traditional rule base approach that lack the advanced approaches such as machine learning and deep learning and often result in poor prediction accuracy. In this study a new method or model for the prediction of selective sweep has been presented. This method has been initiated with simulation, preceded through feature extraction and selection and finally fed to different machine learning algorithms. Here eight different machine learning based methods have been implemented—(1) Support Vector Machine (SVM), (2) Regression Tree, (3) Random Forest, (4) Naive Bayes, (5) Multiple logistic regression, (6) K-Nearest Neighbor (KNN), (7) Gradient boosting and (8) Artificial Neural Network (ANN) and results of their comparative evaluations are presented. It has been observed that random forest model outperformed to its counterparts in terms of evaluation matrices with an area under the ROC (Receiver Operating Characteristic) curve (AUC) score of 0.8448 as well as 1st rank in TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) analysis. Further, a robust model for selective sweep prediction based upon random forest has been developed. Model developed in the current study has outperformed to other existing approaches for prediction and analysis of selective sweep. This new approach for selective sweep analysis is excellent in its accuracy as well as reliability. [ABSTRACT FROM AUTHOR]

Mulching is a widely used agricultural practice that can significantly affect crop growth, yield, and economic outcomes, particularly in regions with varying climatic conditions. The present study evaluated the influence of various mulching practices on the growth, yield, and economic viability of common bean (Phaseolus vulgaris L.) cultivation in Tanzania. The study was conducted across three sites in the eastern agro-ecological zone of Tanzania: Kipera (E4 200-1000 m.a.s.l.), Mgeta (E14 500-000 m.a.s.l.), and Ndole (E2 500-1200 m.a.s.l.). Four mulching treatments--polythene mulch, synthetic biodegradable mulch, rice husk mulch, and a control group--were applied to assess their effects on plant growth and yield components. Results revealed significant variations in growth parameters and yield components across sites. Notably, polythenemulch and synthetic biodegradablemulch consistently outperformed the other treatments. Polythene mulch resulted in an average plant height of 68.37cm, followed closely by synthetic biodegradable mulch at 68.26cm, both significantly (p < 0.05) taller than rice husk mulch (62.79cm) and the control (57.74cm). Canopy coverage was highest with polythene mulch at 61.7%, followed by synthetic biodegradable mulch at 60.5%. Grain yields did not differ significantly between synthetic biodegradable mulch (2.64 t ha-1) and polythene mulch (2.67 t ha-1). Economic analysis indicated that synthetic biodegradable mulch offers promising marginal returns (MR: Tshs. 3,787,450 or USD 1,469) and a benefit-cost ratio (BCR) of 1.91, compared to polythene mulch (MR: Tshs. 4,114,050 or USD 1,595, BCR: 2.06). These findings suggest that synthetic biodegradable mulch is a sustainable and economically viable option for enhancing common bean production across diverse agro-ecological settings in Tanzania. [ABSTRACT FROM AUTHOR]

Human settlement areas significantly impact the environment, leading to changes in both natural and built environments. Comprehensive information on human settlements, particularly in urban areas, is crucial for effective sustainable development planning. However, urban land use investigations are often limited to two-dimensional building footprint maps, neglecting the three-dimensional aspect of building structures. This paper addresses this issue to contribute to Sustainable Development Goal 11, which focuses on making human settlements inclusive, safe, and sustainable. In this study, Sentinel-1 data are used as the primary source to estimate building heights. One challenge addressed is the issue of multiple backscattering in Sentinel-1's signal, particularly in densely populated areas with high-rise buildings. To mitigate this, firstly, Sentinel-1 data from different directions, orbit paths, and polarizations are utilized. Combining ascending and descending orbits significantly improves estimation accuracy, and incorporating a higher number of paths provides additional information. However, Sentinel-1 data alone are not sufficiently rich at a global scale across different orbits and polarizations. Secondly, to enhance the accuracy further, Sentinel-1 data are corrected using nighttime light data as additional information, which shows promising results in addressing multiple backscattering issues. Finally, a deep learning model is trained to generate building height maps using these features, achieving a mean absolute error of around 2 m and a mean square error of approximately 13. The generalizability of this method is demonstrated in several cities with diverse built-up structures, including London, Berlin, and others. Finally, a building height map of Iran is generated and evaluated against surveyed buildings, showcasing its large-scale mapping capability. [ABSTRACT FROM AUTHOR]

Introduction: Despite years of efforts to develop new antibiotics for eradicating multidrug-resistant (MDR) and multi-virulent Methicillin-Resistant Staphylococcus aureus (MRSA) and Vancomycin-Resistant Staphylococcus aureus (VRSA) infections, treatment failures and poor prognoses in most cases have been common. Therefore, there is an urgent need for new therapeutic approaches targeting virulence arrays. Our aim is to discover new anti-virulence therapies targeting MRSA and VRSA virulence arrays. Methodology: We employed phenotypic, molecular docking, and genetic studies to screen for anti-virulence activities among selected promising compounds: Coumarin, Simvastatin, and Ibuprofen. Results: We found that nearly all detected MRSA and VRSA strains exhibited MDR and multi-virulent profiles. The molecular docking results aligned with the phenotypic and genetic assessments of virulence production. Biofilm and hemolysin productions were inhibited, and all virulence genes were downregulated upon treatment with sub-minimum inhibitory concentration (sub-MIC) of these promising compounds. Ibuprofen was the most active compound, exhibiting the highest inhibition and downregulation of virulence gene products. Moreover, in vivo and histopathological studies confirmed these results. Interestingly, we observed a significant decrease in wound area and improvements in re-epithelialization and tissue organization in the Ibuprofen and antimicrobial treated group compared with the group treated with antimicrobial alone. These findings support the idea that a combination of Ibuprofen and antimicrobial drugs may offer a promising new therapy for MRSA and VRSA infections. Conclusion: We hope that our findings can be implemented in clinical practice to assist physicians in making the most suitable treatment decisions. [ABSTRACT FROM AUTHOR]

The high tillage resistance of agricultural soil-engaging tools (TASTs) in farmland operations (e.g., tillage, sowing, crop management, and harvesting) increases fuel consumption and harmful gas emissions, which negatively affect the development of sustainable agriculture. Biomimetic methods are promising and effective technologies for reducing the TASTs and have been developed in the past few years. This review comprehensively summarizes the typical agricultural soil-engaging tools (ASETs) and their characteristics and presents existing biomimetic methods for decreasing TASTs. The introduction of TAST reduction was performed on aspects of tillage, sowing, crop management, and harvesting. The internal mechanisms and possible limitations of current biomimetic methods for various ASETs were investigated. The tillage resistance reduction rates of ASETs, as affected by various biomimetic methods, were quantitatively compared under different soil conditions with statistical analyses. Additionally, three future research directions were recommended in the review to further reduce TASTs and encourage the development of sustainable agriculture. [ABSTRACT FROM AUTHOR]

Autonomous driving systems are a rapidly evolving technology. Trajectory prediction is a critical component of autonomous driving systems that enables safe navigation by anticipating the movement of surrounding objects. Lidar point-cloud data provide a 3D view of solid objects surrounding the ego-vehicle. Hence, trajectory prediction using Lidar point-cloud data performs better than 2D RGB cameras due to providing the distance between the target object and the ego-vehicle. However, processing point-cloud data is a costly and complicated process, and state-of-the-art 3D trajectory predictions using point-cloud data suffer from slow and erroneous predictions. State-of-the-art trajectory prediction approaches suffer from handcrafted and inefficient architectures, which can lead to low accuracy and suboptimal inference times. Neural architecture search (NAS) is a method proposed to optimize neural network models by using search algorithms to redesign architectures based on their performance and runtime. This paper introduces TrajectoryNAS, a novel neural architecture search (NAS) method designed to develop an efficient and more accurate LiDAR-based trajectory prediction model for predicting the trajectories of objects surrounding the ego vehicle. TrajectoryNAS systematically optimizes the architecture of an end-to-end trajectory prediction algorithm, incorporating all stacked components that are prerequisites for trajectory prediction, including object detection and object tracking, using metaheuristic algorithms. This approach addresses the neural architecture designs in each component of trajectory prediction, considering accuracy loss and the associated overhead latency. Our method introduces a novel multi-objective energy function that integrates accuracy and efficiency metrics, enabling the creation of a model that significantly outperforms existing approaches. Through empirical studies, TrajectoryNAS demonstrates its effectiveness in enhancing the performance of autonomous driving systems, marking a significant advancement in the field. Experimental results reveal that TrajcetoryNAS yields a minimum of 4.8 higger accuracy and 1.1* lower latency over competing methods on the NuScenes dataset. [ABSTRACT FROM AUTHOR]

Weeds are one of the primary concerns in agriculture since they compete with crops for nutrients and water, and they also attract insects and pests and are, therefore, hindering crop yield. Moreover, seasonal labour shortages necessitate the automation of such agricultural tasks using machines. For this reason, advances in agricultural robotics have led to many attempts to produce autonomous machines that aim to address the task of weeding both effectively and efficiently. Some of these machines are implementing chemical-based weeding methods using herbicides. The challenge for these machines is the targeted delivery of the herbicide so that the environmental impact of the chemical is minimised. However, environmental concerns drive weeding robots away from herbicide use and increasingly utilise mechanical weeding tools or even laser-based devices. In this case, the challenge is the development and application of effective tools. This paper reviews the progress made in the field of weeding robots during the last decade. Trends during this period are identified, and the current state-of-the-art works are highlighted. Finally, the paper examines the areas where the current technological solutions are still lacking, and recommendations on future directions are made. [ABSTRACT FROM AUTHOR]

Weeds reduce cassava root yields and infest furrow areas quickly. The use of mechanical weeders has been introduced in Thailand; however, manually aligning the weeders with each planting row and at headland turns is still challenging. It is critical to clear weeds on furrow slopes and driveways via mechanical weeders. Automation can support this difficult work for weed management via driveway detection. In this context, deep learning algorithms have the potential to train models to detect driveways through furrow image segmentation. Therefore, the purpose of this research was to develop an image segmentation model for automated weed control operations in cassava plantation fields. To achieve this, image datasets were obtained from various fields to aid weed detection models in automated weed management. Three models—Mask R-CNN, YOLACT, and YOLOv8n-seg—were used to construct the image segmentation model, and they were evaluated according to their precision, recall, and FPS. The results show that YOLOv8n-seg achieved the highest accuracy and FPS (114.94 FPS); however, it experienced issues with frame segmentation during video testing. In contrast, YOLACT had no segmentation issues in the video tests (23.45 FPS), indicating its potential for driveway segmentation in cassava plantations. In summary, image segmentation for detecting driveways can improve weed management in cassava fields, and the further automation of low-cost mechanical weeders in tropical climates can be performed based on the YOLACT algorithm. [ABSTRACT FROM AUTHOR]

Inspired by the natural shape-morphing abilities of biological organisms, we introduce a strategy for creating energy-efficient dynamic 3D metasurfaces through spatiotemporal jamming of interleaved assemblies. Our approach, diverging from traditional shape-morphing techniques reliant on continuous energy inputs, utilizes strategically jammed, paper-based interleaved assemblies. By rapidly altering their stiffness at various spatial points and temporal phases during the relaxation of the soft substrate through jamming, we enable the formation of refreshable, intricate 3D shapes with a desirable load-bearing capability. This process, which does not require ongoing energy consumption, ensures energy-efficient and lasting shape displays. Our theoretical model, linking buckling deformation to residual pre-strain, underpins the inverse design process for an array of interleaved assemblies, facilitating the creation of diverse 3D configurations. This metasurface holds notable potential for tactile displays, particularly for the visually impaired, heralding possibilities in visual impaired education, haptic feedback, and virtual/augmented reality applications. This paper introduces a load-bearing 3D dynamic metasurface that alters the stiffness of interleaved assemblies at various spatial points and temporal phases through jamming. This approach does not require continuous energy input and was demonstrated as a tactile display for the visually impaired. [ABSTRACT FROM AUTHOR]

Gummy stem blight disease (GSB) caused by an ascomycete fungi Didymella bryoniae is one of the most destructive biotic production constraints in muskmelon. Development of resistant cultivars is considered as the most economical and eco-friendly option to manage GSB. In this regard, availability of stable sources of resistance is a pre-requisite. A set of 238 muskmelon genotypes were evaluated for responses to GSB under two different natural infection condition viz., under open field during Kharif 2021 and tunnel condition during late Kharif 2021. Further, 44 identified tolerant and resistant genotypes were screened to confirm the consistency of their tolerant/resistant reaction under artificially created GSB sick plot during summer 2022. None of the genotypes were found to be screened were immune or highly resistant to GSB. However, different degrees of resistance response to GSB were observed in terms of typical symptoms. Two genotypes, 21KGSB-258 and 21KGSB-218 consistently expressed resistant response to GSB under both natural infection and sick plot condition with average disease score of 2 and disease index of 40% considering stem-based disease rating scale. Resistant response of these genotypes could be attributed to delayed appearance of initial symptoms, and lower estimates of PDI and area under disease progressive curve (AUDPC). Hence, two genotypes; 21KGSB-258 and 21KGSB-218 identified as stable and potential sources of GSB resistance, could be used as donors to develop GSB resistant muskmelon cultivars/breeding lines. [ABSTRACT FROM AUTHOR]

Background: Managed populations of the alfalfa leafcutting bee (ALCB), Megachile rotundata (F.), are often not sustainable. In addition to numerous mortality factors that contribute to this, the dense bee populations used to maximize alfalfa pollination quickly deplete floral resources available to bees later in the summer. Providing alternative floral resources as alfalfa declines may help to improve ALCB reproduction. Methods: We examined the relationship between floral resource availability and ALCB reproduction and offspring condition via (1) a field study using alfalfa plots with and without late-blooming wildflower strips to supply food beyond alfalfa bloom, and (2) a field-cage study in which we provided bees with alfalfa, wildflowers, or both as food resources. Results: In the field study, bee cell production closely followed alfalfa floral density with an initial peak followed by large declines prior to wildflower bloom. Few bees visited wildflower strips, whose presence or absence was not associated with any measure of bee reproduction. However, we found that female offspring from cells provisioned earlier in the season, when alfalfa predominated as a source of provisions, eclosed with greater body sizes and proportion body lipids relative to total body mass. For bees restricted to cages, the proportion of offspring that survived to adults was highest on pure alfalfa diets. Adding wildflowers to cages with alfalfa did not affect adult offspring production or female offspring body size and lipid content. Furthermore, although similar numbers of adults were produced on wildflowers alone as with alfalfa alone, females eclosed with smaller body sizes and lower proportion body lipids on wildflowers despite the higher protein content we estimated for wildflower pollen. We found no evidence that adding the late-season wildflower species that we chose to plant enhanced ALCB offspring numbers. Our results highlight the importance of considering multiple measures of reproductive success, including offspring body size and lipid stores, when designing and evaluating floral resource management strategies for agroecosystems. [ABSTRACT FROM AUTHOR]

Vegetables are an essential part of people's daily diet, and weeds can cause serious losses in vegetable yield and quality. Intelligent weeding technology for vegetables will be one of the mainstream technologies in modern agricultural development. This article reviews the current research status of intelligent weeding technology for vegetables, including vegetable and weed detection technology, weeding actuators, and weeding robots. Firstly, the vegetable and weed detection technology was introduced in detail from three aspects: global weed detection, crop-rows detection, and vegetable/weed precise recognition technology. The research results of some researchers were summarised, and the vegetable/weed precise recognition technology, including machine learning and proximal sensor technology, was introduced. Secondly, the weeding actuators and robots were introduced, including intelligent chemical weeding, mechanical weeding, physical weeding, and integrated weed management methods. Some weeding actuators and robots developed by researchers and agricultural companies were showcased. Finally, the challenges and future development directions of intelligent weeding technology were discussed and analysed. Intelligent weeding technology for vegetables is still mainly limited by natural conditions and a lack of technology. In the future, it will be possible to develop in the direction of multi-algorithm and multi-sensor fusion technologies. It is necessary to improve the applicability of intelligent weeding equipment for various environments, crops, and weeds. This article can provide a reference for future research in the field of intelligent weeding for vegetables. [ABSTRACT FROM AUTHOR]

In this short review, corresponding to a talk given at the conference "Cosmology 2023 in Miramare", we combine an analysis of five regions observed by H.E.S.S. in the Galactic Center, intending to constrain the Dark Matter (DM) density profile in a WIMP annihilation scenario. For the analysis, we include the state-of-the-art Galactic diffuse emission Gamma-optimized model computed with DRAGON and a wide range of DM density profiles from cored to cuspy profiles, including different kinds of DM spikes. Our results are able to constrain generalized NFW profiles with an inner slope γ ≳ 1.3 . When considering DM spikes, the adiabatic spike is completely ruled out. However, smoother spikes given by the interactions with the bulge stars are compatible if γ ≲ 0.8 , with an internal slope of γ sp-stars = 1.5 . [ABSTRACT FROM AUTHOR]

VEGF-A is a key cytokine in tumor angiogenesis and a major therapeutic target for cancer. VEGF165 is the predominant isoform of VEGF-A, and it is the most potent angiogenesis stimulant. VEGFR2/KDR domains 2 and 3 (D2D3) bind to the N-terminal domain (NTD, residues 1-110) of VEGF165. Since removal of the heparin-binding domain (HBD, residues 111-165) markedly reduced the mitogenic activity of the growth factor, it has been proposed that the HBD plays a critical role in the mitogenicity of VEGF165. Here, we report that αvβ3 specifically bound to the isolated VEGF165 HBD but not to VEGF165 NTD. Based on docking simulation and mutagenesis, we identified several critical amino acid residues within the VEGF165 HBD required for αvβ3 binding, i.e., Arg123, Arg124, Lys125, Lys140, Arg145, and Arg149. We discovered that VEGF165 HBD binds to the KDR domain 1 (D1) and identified that Arg123 and Arg124 are critical for KDR D1 binding by mutagenesis, indicating that the KDR D1-binding and αvβ3-binding sites overlap in the HBD. Full-length VEGF165 mutant (R123A/R124A/K125A/K140A/R145A/R149A) defective in αvβ3 and KDR D1 binding failed to induce ERK1/2 phosphorylation, integrin β3 phosphorylation, and KDR phosphorylation and did not support proliferation of endothelial cells, although the mutation did not affect the KDR D2D3 interaction with VEGF165. Since β3-knockout mice are known to show enhanced VEGF165 signaling, we propose that the binding of KDR D1 to the VEGF165 HBD and KDR D2D3 binding to the VEGF165 NTD are critically involved in the potent mitogenicity of VEGF165. We propose that binding competition between KDR and αvβ3 to the VEGF165 HBD endows integrin αvβ3 with regulatory properties to act as a negative regulator of VEGF165 signaling. [ABSTRACT FROM AUTHOR]

Background: Mitochondrial health has gained attention in a number of diseases, both as an indicator of disease state and as a potential therapeutic target. The quality and amount of mitochondrial DNA (mtDNA) and RNA (mtRNA) can be important indicators of mitochondrial and cell health, but are difficult to measure in complex tissues. Methods: mtDNA and mtRNA in zebrafish retina samples were fluorescently labeled using RNAscope™ in situ hybridization, then mitochondria were stained using immunohistochemistry. Pretreatment with RNase was used for validation. Confocal images were collected and analyzed, and relative amounts of mtDNA and mtRNA were reported. Findings regarding mtDNA were confirmed using qPCR. Results: Signals from probes detecting mtDNA and mtRNA were localized to mitochondria, and were differentially sensitive to RNase. This labeling strategy allows for quantification of relative mtDNA and mtRNA levels in individual cells. As a demonstration of the method in a complex tissue, single photoreceptors in zebrafish retina were analyzed for mtDNA and mtRNA content. An increase in mtRNA but not mtDNA coincides with proliferation of mitochondria at night in cones. A similar trend was measured in rods. Discussion: Mitochondrial gene expression is an important component of cell adaptations to disease, stress, or aging. This method enables the study of mtDNA and mtRNA in single cells of an intact, complex tissue. The protocol presented here uses commercially-available tools, and is adaptable to a range of species and tissue types. [ABSTRACT FROM AUTHOR]

The Solow residual method, traditionally pivotal for calculating total factor productivity (TFP), is typically not applied to green TFP calculations due to its exclusion of undesired outputs. Diverging from traditional approaches and other frontier methodologies such as Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), this paper integrates undesired outputs and three types of spatial spillover effects into the conventional Solow framework, thereby creating a new spatiotemporal econometric Solow residual method (STE-SRM). Utilizing this novel method, the study computes the industrial green TFPs for 280 Chinese cities from 2003 to 2019, recalculates these TFPs using DEA-SBM and Bayesian SFA for the same cities and periods, and assesses the accuracy of the STE-SRM-derived TFPs through comparative analysis. Additionally, the paper explores the statistical properties of China's urban industrial green TFPs as derived from the STE-SRM, employing Dagum's Gini coefficient and spatial convergence analyses. The findings first indicate that by incorporating undesired outputs and spatial spillover into the Solow residual method, green TFPs are computable in alignment with the traditional Solow logic, although the allocation of per capita inputs and undesired outputs hinges on selecting the optimal empirical production function. Second, China's urban industrial green TFPs, calculated using the STE-SRM with the spatial Durbin model with mixed effects as the optimal model, show that cities like Huangshan, Fangchenggang, and Sanya have notably higher TFPs, whereas Jincheng, Datong, and Taiyuan display lower TFPs. Third, comparisons of China's urban industrial green TFP calculations reveal that those derived from the STE-SRM demonstrate broader but more concentrated results, while Bayesian SFA results are narrower and less concentrated, and DEA-SBM findings sit between these extremes. Fourth, the study highlights significant spatial heterogeneity in China's urban industrial green TFPs across different regions—eastern, central, western, and northeast China—with evident sigma convergence across the urban landscape, though absolute beta convergence is significant only in a limited subset of cities and time periods. [ABSTRACT FROM AUTHOR]

Copyright of Turkish Journal of Sport & Exercise / Türk Spor ve Egzersiz Dergisi is the property of Turkish Journal of Sport & Exercise and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Autonomous navigation in agricultural fields presents a unique challenge due to the unpredictable outdoor environment. Various approaches have been explored to tackle this task, each with its own set of challenges. These include GPS guidance, which faces availability issues and struggles to avoid obstacles, and vision guidance techniques, which are sensitive to changes in light, weeds, and crop growth. This study proposes a novel idea that combining GPS and visual navigation offers an optimal solution for autonomous navigation in agricultural fields. Three solutions for autonomous navigation in cotton fields were developed and evaluated. The first solution utilized a path tracking algorithm, Pure Pursuit, to follow GPS coordinates and guide a mobile robot. It achieved an average lateral deviation of 8.3 cm from the pre-recorded path. The second solution employed a deep learning model, specifically a fully convolutional neural network for semantic segmentation, to detect paths between cotton rows. The mobile rover then navigated using the Dynamic Window Approach (DWA) path planning algorithm, achieving an average lateral deviation of 4.8 cm from the desired path. Finally, the two solutions were integrated for a more practical approach. GPS served as a global planner to map the field, while the deep learning model and DWA acted as a local planner for navigation and real-time decision-making. This integrated solution enabled the robot to navigate between cotton rows with an average lateral distance error of 9.5 cm, offering a more practical method for autonomous navigation in cotton fields. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) ranks as the third most common cancer globally and the second leading cause of cancer-related mortality. Traditional chemotherapy, while effective, often results in significant side effects, highlighting the need for more efficient cancer therapies. Recent advancements in nanotechnology have led to the development of strategies that aim to minimize toxicity to normal cells by more precise targeting of cancer cells. In this context, cobalt oxide nanoparticles (Co3O4 NPs) have shown promising anticancer potential. Our study focuses on evaluating the antioxidant, antibacterial, and anticancer properties of Co3O4 NPs synthesized using Vibrio sp. VLC, a bioluminescent bacterium. Results: XRD and FTIR analyses confirmed the successful synthesis of Co3O4 NPs, which displayed spherical morphology with an average diameter of 60 nm. The nanoparticles demonstrated significant antioxidant and antibacterial activities. The MTT assay indicated that the NPs caused dose- and time-dependent toxicity against CT26 cells, while exhibiting relatively lower toxicity towards normal cells. In vivo experiments further confirmed the significant tumor suppressive effects in BALB/c mice, with minimal side effects on the liver, spleen, and kidney tissues compared to the widespread toxicity of cisplatin. Conclusion: This study verifies the successful synthesis of Co3O4 NPs and their potent antioxidant, antibacterial, and anticancer activities. The biosynthesized Co3O4 NPs represent a promising targeted method for CRC therapy. However, further research is needed to elucidate their mechanism of action and also their application in the clinical phase. [ABSTRACT FROM AUTHOR]

The application of robotics has great implications for future food security, sustainable agricultural development, improving resource efficiency, reducing chemical pesticide use, reducing manual labor, and maximizing field output. Aiming at the problems of high labor intensity and labor shortage in the fields of pesticide application, weeding, and field information collection, a multifunctional and electric field management robot platform is designed, which has four switching steering modes (Ackermann steering, four-wheel steering, crab steering, and zero-radius steering), and its wheel-track can be automatically adjusted. Commonly used spraying booms, weeders, crop information collectors, and other devices can be easily installed on the robot platform. A multi-sensor integrated navigation system including a satellite positioning system, an RGB camera, and a multi-line lidar is designed to realize the unmanned driving of the robot platform in a complex field environment. Field tests have shown that the robot can follow the set route, and tests under simulated conditions have indicated that it can also dynamically correct paths based on crop rows by using a visual system. Results from multiple trials showed that the trajectory tracking accuracy meets the requirements of various field management operations. [ABSTRACT FROM AUTHOR]

Simple Summary: Creative or novel behaviors in bottlenose dolphins (Tursiops truncatus) can be indicators of flexible thinking and problem solving. Twelve bottlenose dolphins (five females, seven males) in managed care were reinforced for exhibiting different behaviors of their choosing in response to a hand gesture. Using a human-based theory of creativity, the dolphins' behaviors were assessed for four aspects: how many different behaviors they could produce in a session or in a row (fluency), how different the behaviors were from each other (flexibility), how simple or complex the behaviors were (elaboration), and how novel or new the behaviors were (originality). The results indicated that dolphins were variable in all aspects measured, with some animals producing more behaviors that were also more complex and variable in type and energy than the other dolphins. Behaviors were also invented by several dolphins. The dolphins were engaged and cognitively challenged by this task, which suggests this task facilitates cognitive welfare while providing a means to study innovative behavior across species. Creative or novel behaviors in bottlenose dolphins (Tursiops truncatus) can be indicators of flexible thinking and problem solving. Over 50 years ago, two rough-tooth dolphins demonstrated creative novel behaviors acquired through reinforcement training in human care. Since this novel training, a variety of species have been trained to respond to this conceptual cue. The current study assessed the creativity of 12 bottlenose dolphins (5 females, 7 males) housed at the Roatan Institute for Marine Sciences (RIMS) in Roatan, Honduras. Individual differences were found across four constructs measured for creativity: fluency, flexibility, elaboration, and originality. Variability in performance occurred across test sessions. Animals with less experience with this task performed fewer "innovative" behaviors as compared to more experienced animals. Despite errors, dolphins continued to attempt the task during test sessions, suggesting the concept of "innovate" was intrinsically rewarding and cognitively engaging. This task may be utilized across species to promote the comparative study of innovative or creative behavior as well as to promote cognitive welfare. [ABSTRACT FROM AUTHOR]

The precision of agro-technical operations is one of the main hallmarks of a modern approach to agriculture. However, ensuring the precise application of plant protection products or the performance of mechanical field operations entails significant costs for sophisticated positioning systems. This paper explores the integration of precision positioning based on the global navigation satellite system (GNSS) in agriculture, particularly in fieldwork operations, seeking solutions of moderate cost with sufficient precision. This study examines the impact of GNSSs on automation and robotisation in agriculture, with a focus on intelligent agricultural guidance. It also discusses commercial devices that enable the automatic guidance of self-propelled machinery and the benefits that they provide. This paper investigates GNSS-based precision localisation devices under real field conditions. A comparison of commercial and low-cost GNSS solutions, along with the integration of satellite navigation with advanced visual odometry for improved positioning accuracy, is presented. The research demonstrates that affordable solutions based on the common differential GNSS infrastructure can be applied for accurate localisation under real field conditions. It also underscores the potential of GNSS-based automation and robotisation in transforming agriculture into a more efficient and sustainable industry. [ABSTRACT FROM AUTHOR]

In recent decades, three major volcanic eruptions of different intensity have occurred (Mount Agung in 1963, El Chichón in 1982 and Mount Pinatubo in 1991), with reported climate impacts on seasonal to decadal timescales that could have been potentially predicted with accurate and timely estimates of the associated stratospheric aerosol loads. The Decadal Climate Prediction Project component C (DCPP-C) includes a protocol to investigate the impact of volcanic aerosols on the climate experienced during the years that followed those eruptions through the use of decadal predictions. The interest of conducting this exercise with climate predictions is that, thanks to the initialisation, they start from the observed climate conditions at the time of the eruptions, which helps to disentangle the climatic changes due to the initial conditions and internal variability from the volcanic forcing. The protocol consists of repeating the retrospective predictions that are initialised just before the last three major volcanic eruptions but without the inclusion of their volcanic forcing, which are then compared with the baseline predictions to disentangle the simulated volcanic effects upon climate. We present the results from six Coupled Model Intercomparison Project Phase 6 (CMIP6) decadal prediction systems. These systems show strong agreement in predicting the well-known post-volcanic radiative effects following the three eruptions, which induce a long-lasting cooling in the ocean. Furthermore, the multi-model multi-eruption composite is consistent with previous work reporting an acceleration of the Northern Hemisphere polar vortex and the development of El Niño conditions the first year after the eruption, followed by a strengthening of the Atlantic Meridional Overturning Circulation the subsequent years. Our analysis reveals that all these dynamical responses are both model- and eruption-dependent. A novel aspect of this study is that we also assess whether the volcanic forcing improves the realism of the predictions. Comparing the predicted surface temperature anomalies in the two sets of hindcasts (with and without volcanic forcing) with observations we show that, overall, including the volcanic forcing results in better predictions. The volcanic forcing is found to be particularly relevant for reproducing the observed sea surface temperature (SST) variability in the North Atlantic Ocean following the 1991 eruption of Pinatubo. [ABSTRACT FROM AUTHOR]

The AIFARMS Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability national AI institute brings together over 40 world‐class AI and agriculture researchers, with the common mission to develop foundational advances in AI and use them to ensure that future agriculture is environmentally friendly, sustainable, affordable, and accessible to diverse farming communities. Since its establishment in 2020, AIFARMS has advanced the state of the art in autonomous farming, cover crop planting, machine learning for improved outcomes from remote sensing, dynamic estimation of yield loss from weeds, and livestock management. The institute has prioritized the creation and utilization of high‐quality, openly available data sets for advancing foundational AI and tackling agricultural challenges. AIFARMS leverages a close partnership between UIUC and Tuskegee University to build programming for a skilled and diverse next‐generation workforce in digital agriculture. We are expanding the reach of AIFARMS outside of the current partners to collaborate with national AI institutions and international partners. [ABSTRACT FROM AUTHOR]

Reliable obstacle avoidance, which is essential for safe autonomous robot interaction with the real world, raises various challenges such as difficulties with obstacle perception and latent factor cognition impacting multi-modal obstacle avoidance. In this paper, we propose a Depth visUal Ego-motion Learning (DUEL) model, consisting of a cognitive generation network, a policy decision network and a potential partition network, to learn autonomous obstacle avoidance from expert policies. The DUEL model takes advantage of binocular vision to perceive scene depth. This serves as the input to the cognitive generation network which generates obstacle avoidance policies by maximizing its causal entropy. The policy decision network then optimizes the generation of the policies referring to expert policies. The generated obstacle avoidance policies are simultaneously transferred to the potential partition network to capture the latent factors contained within expert policies and perform multi-modal obstacle avoidance. These three core networks iteratively optimize the multi-modal policies relying on causal entropy and mutual information theorems, which are proven theoretically. Experimental comparisons with state-of-the-art models on 7 metrics demonstrate the effectiveness of the DUEL model. It achieves the best performance with an average ADE (Average Displacement Error) of 0.29 and average FDE (Final Displacement Error) of 0.55 across five different scenarios. Results show that the DUEL model can maintain an average obstacle avoidance success rate of 97% for both simulated and real world scenarios with multiple obstacles, demonstrating its success at capturing latent factors from expert policies. Our source codes are available at https://github.com/ACoTAI/DUEL. [ABSTRACT FROM AUTHOR]

We demonstrate label-free dynamic optical coherence tomography (D-OCT)-based visualization and quantitative assessment of patterns of tumor spheroid response to three anti-cancer drugs. The study involved treating human breast adenocarcinoma (MCF-7 cell-line) with paclitaxel (PTX), tamoxifen citrate (TAM), and doxorubicin (DOX) at concentrations of 0 (control), 0.1, 1, and 10 µM for 1, 3, and 6 days. In addition, fluorescence microscopy imaging was performed for reference. The D-OCT imaging was performed using a custom-built OCT device. Two algorithms, namely logarithmic intensity variance (LIV) and late OCT correlation decay speed (OCDS l ) were used to visualize the tissue dynamics. The spheroids treated with 0.1 and 1 µM TAM appeared similar to the control spheroid, whereas those treated with 10 µM TAM had significant structural corruption and decreasing LIV and OCDS l over treatment time. The spheroids treated with PTX had decreasing volumes and decrease of LIV and OCDS l signals over time at most PTX concentrations. The spheroids treated with DOX had decreasing and increasing volumes over time at DOX concentrations of 1 and 10 µM, respectively. Meanwhile, the LIV and OCDS l signals decreased over treatment time at all DOX concentrations. The D-OCT, particularly OCDS l , patterns were consistent with the fluorescence microscopic patterns. The diversity in the structural and D-OCT results among the drug types and among the concentrations are explained by the mechanisms of the drugs. The presented results suggest that D-OCT is useful for evaluating the difference in the tumor spheroid response to different drugs and it can be a useful tool for anti-cancer drug testing. [ABSTRACT FROM AUTHOR]