Daily Runoff Simulation of a Coastal Watershed of Southeast China Based on SWAT Model

Integrated Modeling of Hydro-System

Prevalence and determinants of resistant hypertension among hypertensive patients attending a cardiology clinic in China: a prospective cross-sectional study

Purpose: To determine occurrence and determinants of resistant hypertension (RHT) among patients attending cardiology clinic of the affiliated hospital of Hangzhou Normal University, China.Methods: An observational prospective cross-sectional study was conducted among patients with hypertension attending the cardiology clinic over a period of 6 months. After identification of patients with RHT, various independent co-variants were tested by logistic regression in order to evaluate the determinants of RHT.Results: Out of 556 patients, 104 (18.7 %) patients had RHT while 67 (12.1 %) patients had uncontrolled blood pressure (BP) in spite of treatment with three antihypertensive drugs including a diuretic; 37 (6.6 %) patients had controlled BP with > three drugs. Obesity (OR: 2.7, p = 0.002], duration of hypertension (OR: 1.8, p = 0.015], presence of diabetes mellitus (OR: 3.6, p < 0.001) and ischemic heart disease (OR: 3.2, p = 0.001) were significant determinants of resistant hypertension in the study cohort.Conclusion: The prevalence of RHT found in this study is significantly high, thus indicating a need for greater attention of clinicians to this highly morbid condition. Obese patients and those suffering from diabetes mellitus, ischemic heart disease and chronic diseases should be evaluated for the presence of RHT. Early identification of such patients will provide sufficient time for clinicians to refer patients, as well as modify and/or intensify therapy.Keywords: Resistant hypertension, Risk factors, Hypertension, Stroke, Diabetes mellitus, Ischemic heart diseas

Formation Mechanism of Guided Resonances and Bound States in the Continuum in Photonic Crystal Slabs

We develop a formalism, based on the mode expansion method, to describe the guided resonances and bound states in the continuum (BICs) in photonic crystal slabs with one-dimensional periodicity. This approach provides analytic insights to the formation mechanisms of these states: the guided resonances arise from the transverse Fabry-P\'erot condition, and the divergence of the resonance lifetimes at the BICs is explained by a destructive interference of radiation from different propagating components inside the slab. We show BICs at the center and on the edge of the Brillouin zone protected by symmetry, as well as BICs at generic wave vectors not protected by symmetry.Comment: 12 pages, 3 figure

A multipopulation parallel genetic simulated annealing based QoS routing and wavelength assignment integration algorithm for multicast in optical networks

Copyright @ 2008 Elsevier B.V. All rights reserved.In this paper, we propose an integrated Quality of Service (QoS) routing algorithm for optical networks. Given a QoS multicast request and the delay interval specified by users, the proposed algorithm can find a flexible-QoS-based cost suboptimal routing tree. The algorithm first constructs the multicast tree based on the multipopulation parallel genetic simulated annealing algorithm, and then assigns wavelengths to the tree based on the wavelength graph. In the algorithm, routing and wavelength assignment are integrated into a single process. For routing, the objective is to find a cost suboptimal multicast tree. For wavelength assignment, the objective is to minimize the delay of the multicast tree, which is achieved by minimizing the number of wavelength conversion. Thus both the cost of multicast tree and the user QoS satisfaction degree can approach the optimal. Our algorithm also considers load balance. Simulation results show that the proposed algorithm is feasible and effective. We also discuss the practical realization mechanisms of the algorithm.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1, the National Natural Science Foundation of China under Grant nos. 60673159 and 70671020, the National High-Tech Research and Development Plan of China under Grant no. 2006AA01Z214, Program for New Century Excellent Talents in University, and the Key Project of Chinese Ministry of Education under Grant no. 108040

When ChatGPT Meets Smart Contract Vulnerability Detection: How Far Are We?

With the development of blockchain technology, smart contracts have become an important component of blockchain applications. Despite their crucial role, the development of smart contracts may introduce vulnerabilities and potentially lead to severe consequences, such as financial losses. Meanwhile, large language models, represented by ChatGPT, have gained great attentions, showcasing great capabilities in code analysis tasks. In this paper, we presented an empirical study to investigate the performance of ChatGPT in identifying smart contract vulnerabilities. Initially, we evaluated ChatGPT's effectiveness using a publicly available smart contract dataset. Our findings discover that while ChatGPT achieves a high recall rate, its precision in pinpointing smart contract vulnerabilities is limited. Furthermore, ChatGPT's performance varies when detecting different vulnerability types. We delved into the root causes for the false positives generated by ChatGPT, and categorized them into four groups. Second, by comparing ChatGPT with other state-of-the-art smart contract vulnerability detection tools, we found that ChatGPT's F-score is lower than others for 3 out of the 7 vulnerabilities. In the case of the remaining 4 vulnerabilities, ChatGPT exhibits a slight advantage over these tools. Finally, we analyzed the limitation of ChatGPT in smart contract vulnerability detection, revealing that the robustness of ChatGPT in this field needs to be improved from two aspects: its uncertainty in answering questions; and the limited length of the detected code. In general, our research provides insights into the strengths and weaknesses of employing large language models, specifically ChatGPT, for the detection of smart contract vulnerabilities

Flower ontogenesis and fruit development of Synsepalum dulcificum

Synsepalum dulcificum from the family Sapotaceae is known as miracle fruit and is a valuable horticultural species. All plant parts are of medicinal importance whereas the fruit known as magic berry, miracle berry, or sweet berry is consumed fresh. Surprisingly, very little is known on the species in terms of flower morphology and flower development. In this study, an observation on the flower morphology and flower development of miracle fruit has been made with the aid of microscopic techniques. Miracle fruit flower requires 100 days to develop from reproductive meristem to full anthesis. The flower development can be divided into six stages based on the size and appearance of the flower bud. The fruit with persistent style developed and ripened 90 days after anthesis. Heavy fruit drop was observed at 40–60 days after anthesis which contributed to the final fruit set of average of 5.06% per plant. Through this study, miracle fruit is strongly insect pollinated and prevents self-fertilization. A study on pollination ecology is needed to identify the pollinator for miracle fruit, as this is important in manipulating fruit loading in the future

Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators

Molecular dynamics simulations have emerged as a fundamental instrument for studying biomolecules. At the same time, it is desirable to perform simulations of a collection of particles under various conditions in which the molecules can fluctuate. In this paper, we explore and adapt the soft prompt-based learning method to molecular dynamics tasks. Our model can remarkably generalize to unseen and out-of-distribution scenarios with limited training data. While our work focuses on temperature as a test case, the versatility of our approach allows for efficient simulation through any continuous dynamic conditions, such as pressure and volumes. Our framework has two stages: 1) Pre-trains with data mixing technique, augments molecular structure data and temperature prompts, then applies a curriculum learning method by increasing the ratio of them smoothly. 2) Meta-learning-based fine-tuning framework improves sample-efficiency of fine-tuning process and gives the soft prompt-tuning better initialization points. Comprehensive experiments reveal that our framework excels in accuracy for in-domain data and demonstrates strong generalization capabilities for unseen and out-of-distribution samples