Dry mass input into fruits can be predicted by fine root morphology of pepper cultivars exposed to varied lighting spectra

Many pepper cultivars can be raised under artificial lighting in a plant factory. An easily measured parameter is needed to fast predict fruit loading in pepper cultivars. In this study, four pepper cultivars with contrasting manners in growth and fruiting were cultured under three light-emitting diode (LED) spectra in comparison with a sunlight control. It was found that the red-light spectrum (71.7% red-, 13.7% green-, 14.6% blue-lights) increased over 40% of dry mass in fruits, while the green-light spectrum (26.2% red-, 56.4% green-, 17.4% blue-lights) induced no fruiting compared to the control. Only two cultivars responded by fine root morphology, which was characterized as smaller surface-area and fewer tip-number in the blue-light spectrum (7.8% red-, 33.7% green-, 48.5% blue-lights) than in red LED light. Tip-number showed a negative correlation with fruit dry-mass in three cultivars, while fine root diameter increased with dry mass in fruits. In conclusion, fine root tip-number can be used as a predictor of fruit dry-mass in pepper cultivars high in fruit quality or yield. The red-colour light was recommended for raising pepper cultivars in a plant factory with the purpose of greater fruit productivity

Repeated Loading Model for Elastic-Plastic Contact of Geomaterial

A new nonlinear hysteretic model with considering the loading, unloading, and reloading processes is developed based on Drucker—Prager yield criterion and finite-element analysis. This model can be used for multiple repeated elastic—plastic normal direction contact problems between two identical spherical geomaterials. After examining the influence of material properties, strain hardening, and loading histories, we found that the hysteretic phenomena (represented by residual displacement and plastic work) become weak after the first cycle, and the subsequent cycles step into elastic shakedown state eventually. A critical number of cycles can be used to estimate the state of ratchetting, plastic shakedown, as well as elastic shakedown. It also found that the subsequent curves will be stiffer than the previous ones, especially when the yield strength is high and ratchetting effect is not strong. This new model can be used for a wide range of geomaterials under different loading levels, and it can also be extended to describe the constitutive behavior of spheres under earthquake as well as aftershocks

Towards Accurate One-Stage Object Detection with AP-Loss

One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously, with the former suffering much from extreme foreground-background class imbalance issue due to the large number of anchors. This paper alleviates this issue by proposing a novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the Average-Precision loss (AP-loss) for the ranking problem. Due to its non-differentiability and non-convexity, the AP-loss cannot be optimized directly. For this purpose, we develop a novel optimization algorithm, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks. We verify good convergence property of the proposed algorithm theoretically and empirically. Experimental results demonstrate notable performance improvement in state-of-the-art one-stage detectors based on AP-loss over different kinds of classification-losses on various benchmarks, without changing the network architectures. Code is available at https://github.com/cccorn/AP-loss.Comment: 13 pages, 7 figures, 4 tables, main paper + supplementary material, accepted to CVPR 201

A novel gas ionization sensor using Pd nanoparticle-capped ZnO

A novel gas ionization sensor using Pd nanoparticle-capped ZnO (Pd/ZnO) nanorods as the anode is proposed. The Pd/ZnO nanorod-based sensors, compared with the bare ZnO nanorod, show lower breakdown voltage for the detected gases with good sensitivity and selectivity. Moreover, the sensors exhibit stable performance after more than 200 tests for both inert and active gases. The simple, low-cost, Pd/ZnO nanorod-based field-ionization gas sensors presented in this study have potential applications in the field of gas sensor devices

Emergency Resource Layout with Multiple Objectives under Complex Disaster Scenarios

Effective placement of emergency rescue resources, particularly with joint suppliers in complex disaster scenarios, is crucial for ensuring the reliability, efficiency, and quality of emergency rescue activities. However, limited research has considered the interaction between different disasters and material classification, which are highly vital to the emergency rescue. This study provides a novel and practical framework for reliable strategies of emergency rescue under complex disaster scenarios. The study employs a scenario-based approach to represent complex disasters, such as earthquakes, mudslides, floods, and their interactions. In optimizing the placement of emergency resources, the study considers government-owned suppliers, framework agreement suppliers, and existing suppliers collectively supporting emergency rescue materials. To determine the selection of joint suppliers and their corresponding optimal material quantities under complex disaster scenarios, the research proposes a multi-objective model that integrates cost, fairness, emergency efficiency, and uncertainty into a facility location problem. Finally, the study develops an NSGA-II-XGB algorithm to solve a disaster-prone province example and verify the feasibility and effectiveness of the proposed multi-objective model and solution methods. The results show that the methodology proposed in this paper can greatly reduce emergency costs, rescue time, and the difference between demand and suppliers while maximizing the coverage of rescue resources. More importantly, it can optimize the scale of resources by determining the location and number of materials provided by joint suppliers for various kinds of disasters simultaneously. This research represents a promising step towards making informed configuration decisions in emergency rescue work

A Simple Model for Elastic-Plastic Contact of Granular Geomaterials

We propose a simple elastic-plastic contact model by considering the interaction of two spheres in the normal direction, for use in discrete element method (DEM) simulations of geomaterials. This model has been developed by using the finite element method (FEM) and nonlinear fitting methods, in the form of power-law relation of the dimensionless normal force and displacement. Only four parameters are needed for each loading-unloading contact process between two spheres, which are relevant to material properties evaluated by FEM simulations. Within the given range of material properties, those four parameters can be quickly accessed by interpolating the data appended or by regression functions supplied. Instead of the Von Mises (V-M) yield criterion, the Drucker-Prager (D-P) criterion is used to describe the yield behavior of contacting spheres in this model. The D-P criterion takes the effects of confining pressure, the intermediate principal stress, and strain rate into consideration; thus, this model can be used for DEM simulation of geomaterials as well as other granular materials with pressure sensitivity

Generative artificial intelligence-enabled dynamic detection of nicotine-related circuits

The identification of addiction-related circuits is critical for explaining addiction processes and developing addiction treatments. And models of functional addiction circuits developed from functional imaging are an effective tool for discovering and verifying addiction circuits. However, analyzing functional imaging data of addiction and detecting functional addiction circuits still have challenges. We have developed a data-driven and end-to-end generative artificial intelligence(AI) framework to address these difficulties. The framework integrates dynamic brain network modeling and novel network architecture networks architecture, including temporal graph Transformer and contrastive learning modules. A complete workflow is formed by our generative AI framework: the functional imaging data, from neurobiological experiments, and computational modeling, to end-to-end neural networks, is transformed into dynamic nicotine addiction-related circuits. It enables the detection of addiction-related brain circuits with dynamic properties and reveals the underlying mechanisms of addiction

Local Feature Matching Using Deep Learning: A Survey

Local feature matching enjoys wide-ranging applications in the realm of computer vision, encompassing domains such as image retrieval, 3D reconstruction, and object recognition. However, challenges persist in improving the accuracy and robustness of matching due to factors like viewpoint and lighting variations. In recent years, the introduction of deep learning models has sparked widespread exploration into local feature matching techniques. The objective of this endeavor is to furnish a comprehensive overview of local feature matching methods. These methods are categorized into two key segments based on the presence of detectors. The Detector-based category encompasses models inclusive of Detect-then-Describe, Joint Detection and Description, Describe-then-Detect, as well as Graph Based techniques. In contrast, the Detector-free category comprises CNN Based, Transformer Based, and Patch Based methods. Our study extends beyond methodological analysis, incorporating evaluations of prevalent datasets and metrics to facilitate a quantitative comparison of state-of-the-art techniques. The paper also explores the practical application of local feature matching in diverse domains such as Structure from Motion, Remote Sensing Image Registration, and Medical Image Registration, underscoring its versatility and significance across various fields. Ultimately, we endeavor to outline the current challenges faced in this domain and furnish future research directions, thereby serving as a reference for researchers involved in local feature matching and its interconnected domains. A comprehensive list of studies in this survey is available at https://github.com/vignywang/Awesome-Local-Feature-Matching .Comment: Accepted by Information Fusion 2024. Project page: https://github.com/vignywang/Awesome-Local-Feature-Matchin

Dry mass input into fruits can be predicted by fine root morphology of pepper cultivars exposed to varied lighting spectra

Many pepper cultivars can be raised under artificial lighting in a plant factory. An easily measured parameter is needed to fast predict fruit loading in pepper cultivars. In this study, four pepper cultivars with contrasting manners in growth and fruiting were cultured under three light-emitting diode (LED) spectra in comparison with a sunlight control. It was found that the red-light spectrum (71.7% red-, 13.7% green-, 14.6% blue-lights) increased over 40% of dry mass in fruits, while the green-light spectrum (26.2% red-, 56.4% green-, 17.4% blue-lights) induced no fruiting compared to the control. Only two cultivars responded by fine root morphology, which was characterized as smaller surface-area and fewer tip-number in the blue-light spectrum (7.8% red-, 33.7% green-, 48.5% blue-lights) than in red LED light. Tip-number showed a negative correlation with fruit dry-mass in three cultivars, while fine root diameter increased with dry mass in fruits. In conclusion, fine root tip-number can be used as a predictor of fruit dry-mass in pepper cultivars high in fruit quality or yield. The red-colour light was recommended for raising pepper cultivars in a plant factory with the purpose of greater fruit productivity