Distribution of Onryza maga (Leech, 1890) (Lepidoptera: Hesperiidae) with description of female genitalia and taxonomic notes

For more than twenty years, Hainan, Vietnam, Myanmar, Thailand, Malaysia, Singapore and Indonesia have been erroneously reported in Chinese literature as belonging to the distribution range of Onryza maga (Leech 1890). Based upon a careful survey of specimens and relevant literature, these regions are omitted from the known range of this species. Onryza maga maga is found from northeast Guizhou, south Henan and Qinling-Daba Mountains in Shaanxi of China, its occurrence in Hunan is confirmed. The adults are redescribed and the variability of wing patterns is discussed. Female genitalia are illustrated and described for the first time. Some biological information and an updated distribution map of the species are provided

Highly Efficient and Reversible Iodine Capture in Hexaphenylbenzene-Based Conjugated Microporous Polymers

The effective and safe capture and storage of radioactive iodine (¹²⁹I or ¹³¹I) is of significant importance during nuclear waste storage and nuclear energy generation. Here we present detailed evidence of highly efficient and reversible iodine capture in hexaphenyl­benzene-based conjugated microporous polymers (HCMPs), synthesized via Buchwald–Hartwig (BH) cross-coupling of a hexakis­(4-bromo­phenyl)­benzene (HBB) core and aryl diamine linkers. The HCMPs present moderate surface areas up to 430 m² g^–1, with narrow pore size distribution and uniform ultramicropore sizes of less than 1 nm. Porous properties are controlled by the strut lengths and rigidities of linkers, while porosity and uptake properties can be tuned by changing the oxidation state of the HCMPs. The presence of a high number of amine functional groups combined with microporosity provides the HCMPs with extremely high iodine affinity with uptake capacities up to 336 wt %, which is to the best of our knowledge the highest reported to date. Two ways to release the adsorbed iodine were explored: either slow release into ethanol or quick release upon heating (with a high degree of control). Spectral studies indicate that the combination of microporosity, amine functionality, and abundant π-electrons ensured well-defined host–guest interactions and controlled uptake of iodine. In addition, the HCMPs could be recycled while maintaining 90% iodine uptake capacity (up to 295%). We envisage wider application of these materials in the facile uptake and removal of unwanted oxidants from the environment

Hypersonic Vehicles Profile-Following Based on LQR Design Using Time-Varying Weighting Matrices

In the process of applying linear quadratic regulator (LQR) to solve aerial vehicle reentry reference trajectory guidance, to obtain better profile-following performance, the parameters of the aerial vehicle system can be used to calculate weighting matrices according to the Bryson principle. However, the traditional method is not applicable to various disturbances in hypersonic vehicles (HSV) which have particular dynamic characteristics. By calculating the weighting matrices constructed based on Bryson principle using time-varying parameters, a novel time-varying LQR design method is proposed to deal with the various disturbances in HSV reentry profile-following. Different from the previous approaches, the current states of the flight system are employed to calculate the parameters in weighting matrices. Simulation results are given to demonstrate that using the proposed approach in this chapter, performance of HSV profile-following can be improved significantly, and stronger robustness against different disturbances can be obtained

CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study

Participant recruitment based on unstructured medical texts such as clinical notes and radiology reports has been a challenging yet important task for the cohort establishment in clinical research. Recently, Large Language Models (LLMs) such as ChatGPT have achieved tremendous success in various downstream tasks thanks to their promising performance in language understanding, inference, and generation. It is then natural to test their feasibility in solving the cohort recruitment task, which involves the classification of a given paragraph of medical text into disease label(s). However, when applied to knowledge-intensive problem settings such as medical text classification, where the LLMs are expected to understand the decision made by human experts and accurately identify the implied disease labels, the LLMs show a mediocre performance. A possible explanation is that, by only using the medical text, the LLMs neglect to use the rich context of additional information that languages afford. To this end, we propose to use a knowledge graph as auxiliary information to guide the LLMs in making predictions. Moreover, to further boost the LLMs adapt to the problem setting, we apply a chain-of-thought (CoT) sample selection strategy enhanced by reinforcement learning, which selects a set of CoT samples given each individual medical report. Experimental results and various ablation studies show that our few-shot learning method achieves satisfactory performance compared with fine-tuning strategies and gains superb advantages when the available data is limited. The code and sample dataset of the proposed CohortGPT model is available at: https://anonymous.4open.science/r/CohortGPT-4872/Comment: 16 pages, 10 figure

Optimum Pipeline for Visual Terrain Classification Using Improved Bag of Visual Words and Fusion Methods

We propose an optimum pipeline and develop the hybrid representation to produce an effective and efficient visual terrain classification system. The bag of visual words (BOVW) framework has emerged as a promising approach and effective paradigm for visual terrain classification. The method includes four main steps: (1) feature extraction, (2) codebook generation, (3) feature coding, and (4) pooling and normalization. Recent researches have primarily focused on feature extraction in the development of new handcrafted descriptors that are specific to the visual terrain. However, the effects of other steps on visual terrain classification are still unknown. At the same time, fusion methods are often used to boost classification performance by exploring the complementarity of diverse features. We provide a comprehensive study of all steps in the BOVW framework and different fusion methods for visual terrain classification. Then, multiple approaches in each step and their effects are explored on the visual terrain dataset. Finally, the feature preprocessing technique, improved BOVW framework, and fusion method are used to construct an optimum pipeline for visual terrain classification. The hybrid representation developed by the optimum pipeline performs effectively and rapidly for visual terrain classification in the terrain dataset, outperforming those current methods. Furthermore, it is robust to diverse noises and illumination alterations

A novel unambiguous strategy of molecular feature extraction in machine learning assisted predictive models for environmental properties

Environmental properties of compounds provide significant information in treating organic pollutants, which drives the chemical process and environmental science toward eco-friendly technology. Traditional group contribution methods play an important role in property estimations, whereas various disadvantages emerge in their applications, such as scattered predicted values for certain groups of compounds. In order to address such issues, an extraction strategy for molecular features is proposed in this research, which is characterized by interpretability and discriminating power with regard to isomers. Based on the Henry's law constant data of organic compounds in water, we developed a hybrid predictive model that integrates the proposed strategy in conjunction with a neural network framework. The structure of the predictive model is optimized using cross-validation and grid search to improve its robustness. Moreover, the predictive model is improved by introducing the plane of best fit descriptor as input and adopting k-means clustering in sampling. In contrast with reported models in the literature, the developed predictive model demonstrates improved generality, higher accuracy, and fewer molecular features used in its development

Search for light dark matter from atmosphere in PandaX-4T

We report a search for light dark matter produced through the cascading decay of $\eta$ mesons, which are created as a result of inelastic collisions between cosmic rays and Earth's atmosphere. We introduce a new and general framework, publicly accessible, designed to address boosted dark matter specifically, with which a full and dedicated simulation including both elastic and quasi-elastic processes of Earth attenuation effect on the dark matter particles arriving at the detector is performed. In the PandaX-4T commissioning data of 0.63 tonne$\cdot$year exposure, no significant excess over background is observed. The first constraints on the interaction between light dark matter generated in the atmosphere and nucleus through a light scalar mediator are obtained. The lowest excluded cross-section is set at $5.9 \times 10^{-37}{\rm cm^2}$ for dark matter mass of $0.1$ MeV$/c^2$ and mediator mass of 300 MeV$/c^2$. The lowest upper limit of $\eta$ to dark matter decay branching ratio is $1.6 \times 10^{-7}$