Generalized linear mixed quantile regression with panel data

A new generalized linear mixed quantile model for panel data is proposed. This proposed approach applies GEE with smoothed estimating functions, which leads to asymptotically equivalent estimation of the regression coefficients. Random effects are predicted by using the best linear unbiased predictors (BLUP) based on the Tweedie exponential dispersion distributions which cover a wide range of distributions, including those widely used ones, such as the normal distribution, Poisson distribution and gamma distribution. A Taylor expansion of the quantile estimating function is used to linearize the random effects in the quantile process. The parameter estimation is based on the Newton-Raphson iteration method. Our proposed quantile mixed model gives consistent estimates that have asymptotic normal distributions. Simulation studies are carried out to investigate the small sample performance of the proposed approach. As an illustration, the proposed method is applied to analyze the epilepsy data

The Establishment Risk of Lycorma delicatula (Hemiptera: Fulgoridae) in the United States and Globally

Native to Asia, the spotted lanternfly, Lycorma delicatula (White), is an emerging pest of many commercially important plants in Korea, Japan, and the United States. Determining its potential distribution is important for proactive measures to protect commercially important commodities. The objective of this study was to assess the establishment risk of L. delicatula globally and in the United States using the ecological niche model MAXENT, with a focus on Washington State (WA), where large fruit industries exist. The MAXENT model predicted highly suitable areas for L. delicatula in Asia, Oceania, South America, North America, Africa, and Europe, but also predicted that tropical habitats are not suitable for its establishment, contrary to published information. Within the United States, the MAXENT model predicted that L. delicatula can establish in most of New England and the mid-Atlantic states, the central United States and the Pacific Coast states, including WA. If introduced, L. delicatula is likely to establish in fruit-growing regions of the Pacific Northwest. The most important environmental variables for predicting the potential distribution of L. delicatula were mean temperature of driest quarter, elevation, degree-days with a lower developmental threshold value of 11°C, isothermality, and precipitation of coldest quarter. Results of this study can be used by regulatory agencies to guide L. delicatula surveys and prioritize management interventions for this pest

PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation

The Segment Anything Model (SAM) has exhibited outstanding performance in various image segmentation tasks. Despite being trained with over a billion masks, SAM faces challenges in mask prediction quality in numerous scenarios, especially in real-world contexts. In this paper, we introduce a novel prompt-driven adapter into SAM, namely Prompt Adapter Segment Anything Model (PA-SAM), aiming to enhance the segmentation mask quality of the original SAM. By exclusively training the prompt adapter, PA-SAM extracts detailed information from images and optimizes the mask decoder feature at both sparse and dense prompt levels, improving the segmentation performance of SAM to produce high-quality masks. Experimental results demonstrate that our PA-SAM outperforms other SAM-based methods in high-quality, zero-shot, and open-set segmentation. We're making the source code and models available at https://github.com/xzz2/pa-sam.Comment: Code is available at https://github.com/xzz2/pa-sa

Protected agriculture matters: Year-round persistence of Tuta absoluta in China where it should not

Tuta absoluta (Lepidoptera: Gelechiidae) originates from the South American tropics but has become a major invasive pest of tomato and other Solanaceae crops worldwide. Agricultural protected facilities (APFs) such as greenhouses and plastic tunnels may provide thermal conditions that allow the survival of T. absoluta in temperate zones with cold winters. In this study, a CLIMEX model was used to investigate the dual effects of increasing use of APFs and climate warming on the potential distribution and seasonal dynamics of T. absoluta in China. Our model showed that the northern boundary for year-round population persistence in China, ignoring APFs, was approximately 30°N, covering about 21% of China’s area suitable under current climate. The modelled suitable area increased to 31% and northern boundary for year-round population persistence shifted to 40°N in 2080 under global warming. When APF refuges are included, the potential suitable area was 78% under the current climate and 79% under global warming. This suggests that, in the future, the increasing use of APFs will increase the areas at risk of T. absoluta invasion significantly more than global warming because APFs effectively protect T. absoluta from harsh northern winters. In addition, vegetable production in surrounding open fields will be at risk of invasion during milder seasons when APFs are opened and T. absoluta can disperse. Therefore, the micro-climate of APFs should be considered as part of the invasion process, and Integrated Pest Management should be simultaneously implemented inside and outside APFs for the rational management T. absoluta.This work was supported by National Key R&D program of China (2021YFD1400200). CERCA Program / Generalitat de Catalunya provided funding to JA, and ND was funded in part by the Horizon Europe project ADOPT-IPM (n◦101060430).info:eu-repo/semantics/publishedVersio

The Establishment Risk of Lycorma delicatula (Hemiptera: Fulgoridae) in the United States and Globally

Native to Asia, the spotted lanternfly, Lycorma delicatula (White), is an emerging pest of many commercially important plants in Korea, Japan, and the United States. Determining its potential distribution is important for proactive measures to protect commercially important commodities. The objective of this study was to assess the establishment risk of L. delicatula globally and in the United States using the ecological niche model MAXENT, with a focus on Washington State (WA), where large fruit industries exist. The MAXENT model predicted highly suitable areas for L. delicatula in Asia, Oceania, South America, North America, Africa, and Europe, but also predicted that tropical habitats are not suitable for its establishment, contrary to published information. Within the United States, the MAXENT model predicted that L. delicatula can establish in most of New England and the mid-Atlantic states, the central United States and the Pacific Coast states, including WA. If introduced, L. delicatula is likely to establish in fruit-growing regions of the Pacific Northwest. The most important environmental variables for predicting the potential distribution of L. delicatula were mean temperature of driest quarter, elevation, degree-days with a lower developmental threshold value of 11°C, isothermality, and precipitation of coldest quarter. Results of this study can be used by regulatory agencies to guide L. delicatula surveys and prioritize management interventions for this pest

Improvement of Stability in an Oscillating Water Column Wave Energy Using an Adaptive Intelligent Controller

Presently, among the global ocean energy technologies, the most conventional one is the wave energy power generation device based on the oscillating water column (OWC) wave energy converter. Given the fluctuation and randomness of waves and the complexity of the current power grid, the dynamic response of grid connections must be considered. Furthermore, considering the characteristics of the wave energy converter, this paper proposed an adaptive intelligent controller (AIC) for the permanent magnet synchronous generator (PMSG) in an OWC. The proposed controller includes a grey predictor, a recurrent wavelet-based Elman neural network (RWENN), and an adaptive critical network (ACN) to improve the stability of OWC power generation. This scheme can increase the maximum power output and improve dynamic performance when a transient occurs under the operating conditions of random wave changes. The proposed AIC for the PMSG based on OWC has a faster response speed, a smaller overshoot, and better stability than the traditional PI controller. This further verifies the availability of the proposed control strategy

Application of information technologies in monitoring the population density of pests

Monitoring pest population density is the key to prediction precision, which has the significant guidance for integrated pest management in agricultural ecosystem. Based on the traditional monitoring technique of pests, the progress of monitoring pest density with modern information technologies was reviewed. The advantages and disadvantages in application of computer vision technology (CVT), acoustic technique, radar and remote sensing technology to estimate the pest population density were analyzed. CVT and sensor will be powerful and potential tools in assessing the pest density. The pathway and main prospects were expounded in automatic acquisition of the data of pest density. Those modern technologies were significant for pest prediction and integrated pest management (IPM) in the future. (41 refs.

Hierarchically and Cooperatively Learning Traffic Signal Control

Deep reinforcement learning (RL) has been applied to traffic signal control recently and demonstrated superior performance to conventional control methods. However, there are still several challenges we have to address before fully applying deep RL to traffic signal control. Firstly, the objective of traffic signal control is to optimize average travel time, which is a delayed reward in a long time horizon in the context of RL. However, existing work simplifies the optimization by using queue length, waiting time, delay, etc., as immediate reward and presumes these short-term targets are always aligned with the objective. Nevertheless, these targets may deviate from the objective in different road networks with various traffic patterns. Secondly, it remains unsolved how to cooperatively control traffic signals to directly optimize average travel time. To address these challenges, we propose a hierarchical and cooperative reinforcement learning method-HiLight. HiLight enables each agent to learn a high-level policy that optimizes the objective locally by selecting among the sub-policies that respectively optimize short-term targets. Moreover, the high-level policy additionally considers the objective in the neighborhood with adaptive weighting to encourage agents to cooperate on the objective in the road network. Empirically, we demonstrate that HiLight outperforms state-of-the-art RL methods for traffic signal control in real road networks with real traffic