Regulation of South China Sea throughflow by pressure difference

Author Posting. © American Geophysical Union, 2016. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 121 (2016): 4077–4096, doi:10.1002/2015JC011177.Sea Surface Height (SSH) data from the European Centre for Medium-Range Weather Forecasts-Ocean Reanalysis System 4 (ECMWF-ORAS4) are used to determine the pressure difference in connection with variability of the South China Sea ThroughFlow (SCSTF) from 1958 to 2007. Two branches of SCSTF, the Karimata-Sunda Strait ThroughFlow (KSSTF) and the Mindoro Strait ThroughFlow (MSTF), are examined. Using the ensemble empirical mode decomposition method (EEMD), time series of pressure difference and volume transport are decomposed into intrinsic mode functions and trend functions, with the corresponding variability on different time scales. Pressure difference agrees with the KSSTF volume transport on decadal time scale; while for the MSTF, pressure difference varies similarly with volume transport on interannual time scale. Separating the dynamic height difference into the thermal and haline terms, for the KSSTF more than half of the dynamic height difference (32 cm) is due to the thermal contributions; while the remaining dynamic height difference (23 cm) is due to the haline contributions. For the MSTF, the dynamic height difference (29 cm) is primarily due to the thermal contribution (26 cm).This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant XDA11010304), the National Natural Science Foundation of China (grant number 41306015 and 41476013) and the Independent Research Project Program of State Key Laboratory of Tropical Oceanography (grant LTOZZ1603).2016-12-1

Non-Pareto Genetic Algorithm for Optimal Planning of Multi-Type Energy Resources in Active Distribution Networks

Based on summarizing and analyzing the typical applications of energy storage, the study established a model for an active distribution network, and analyzed the technical and economic benefits of its access to the distribution network. In addition, considering the economic and technical requirements of multiple types of energy, ensure the stable and continuous operation of multiple types of energy, and build an optimal configuration model for multiple types of energy. To achieve a reliable solution to the model, a non-Pareto genetic algorithm (NSGA-II) is designed to obtain the optimal Pareto solution set for multi-type energy location and volume schemes. The proposed solution algorithm has a rich individual update mechanism and an advanced Pareto solution set storage and screening mechanism, which can effectively solve the problem. Furthermore, idea point decision making (IPDM) has been designed to select the best compromise solution in Pareto non-dominated solution set. Finally, based on the IEEE-33 node standard test system, the input source-load uncertainty scenario set is used to construct the distribution network operation scenario, and the configuration model is solved. The results show that NSGA-II can obtain a Pareto front with better solution quality and a more uniform distribution. After accessing the battery energy storage systems (BESS), the annual total power fluctuation and peak-valley difference of daily maximum load have been reduced by 19.25% and 11.8% respectively

InstructBio: A Large-scale Semi-supervised Learning Paradigm for Biochemical Problems

In the field of artificial intelligence for science, it is consistently an essential challenge to face a limited amount of labeled data for real-world problems. The prevailing approach is to pretrain a powerful task-agnostic model on a large unlabeled corpus but may struggle to transfer knowledge to downstream tasks. In this study, we propose InstructMol, a semi-supervised learning algorithm, to take better advantage of unlabeled examples. It introduces an instructor model to provide the confidence ratios as the measurement of pseudo-labels' reliability. These confidence scores then guide the target model to pay distinct attention to different data points, avoiding the over-reliance on labeled data and the negative influence of incorrect pseudo-annotations. Comprehensive experiments show that InstructBio substantially improves the generalization ability of molecular models, in not only molecular property predictions but also activity cliff estimations, demonstrating the superiority of the proposed method. Furthermore, our evidence indicates that InstructBio can be equipped with cutting-edge pretraining methods and used to establish large-scale and task-specific pseudo-labeled molecular datasets, which reduces the predictive errors and shortens the training process. Our work provides strong evidence that semi-supervised learning can be a promising tool to overcome the data scarcity limitation and advance molecular representation learning

SurrealDriver: Designing Generative Driver Agent Simulation Framework in Urban Contexts based on Large Language Model

Simulation plays a critical role in the research and development of autonomous driving and intelligent transportation systems. However, the current simulation platforms exhibit limitations in the realism and diversity of agent behaviors, which impede the transfer of simulation outcomes to the real world. In this paper, we propose a generative driver agent simulation framework based on large language models (LLMs), capable of perceiving complex traffic scenarios and providing realistic driving maneuvers. Notably, we conducted interviews with 24 drivers and used their detailed descriptions of driving behavior as chain-of-thought prompts to develop a `coach agent' module, which can evaluate and assist driver agents in accumulating driving experience and developing human-like driving styles. Through practical simulation experiments and user experiments, we validate the feasibility of this framework in generating reliable driver agents and analyze the roles of each module. The results show that the framework with full architect decreased the collision rate by 81.04% and increased the human-likeness by 50%. Our research proposes the first urban context driver agent simulation framework based on LLMs and provides valuable insights into the future of agent simulation for complex tasks.Comment: 12 pages, 8 figure

Changes in axial length in anisometropic children wearing orthokeratology lenses

PurposeThere is a particular anisometropia occurring in one eye with myopia, while the other eye has very low myopia, emmetropia, or very low hyperopia. It is unclear how the binocular axial length changes when these children wear unilateral OK lenses only in the more myopic eyes. This study investigates the changes in the axial elongation of both eyes.MethodsThis is a 1-year retrospective study. In total, 148 children with myopic anisometropia were included. The more myopic eyes were wearing orthokeratology lenses (treated eyes), whereas the contralateral eyes were not indicated for visual correction (untreated eyes). The untreated eyes were classified into three subgroups based on the spherical equivalent refraction (SER): low myopia (≤ -0.50 D, n = 37), emmetropia (+0.49 to −0.49 D, n = 76), and low hyperopia (≥0.50 D, n = 35). Changes in the axial length (AL) were compared between the untreated and treated eyes and among the three subgroups.ResultsThe axial elongation was 0.14 ± 0.18 mm and 0.39 ± 0.27 mm in all treated and untreated eyes, respectively (p < 0.001). The interocular AL difference decreased significantly from 1.09 ± 0.45 mm at the baseline to 0.84 ± 0.52 mm at 1 year (p < 0.001). The baseline median (Q1, Q3) SER of the untreated eyes were −0.75 D (−0.56, −0.88 D), 0.00 D (0.00, −0.25 D), and +0.75 D (+1.00, +0.62 D) in low myopia, emmetropia, and low hyperopia subgroups, respectively. The axial elongation was 0.14 ± 0.18 mm, 0.15 ± 0.17 mm, and 0.13 ± 0.21 mm (p = 0.92) in the treated eyes and 0.44 ± 0.25 mm, 0.35 ± 0.24 mm, and 0.41 ± 0.33 mm in the untreated eyes (p = 0.11) after 1 year. Multivariate linear regression analyses only showed significant differences in axial elongation between the emmetropia and low myopia subgroups of untreated eyes (p = 0.04; p > 0.05 between other subgroups).ConclusionUnilateral orthokeratology lenses effectively reduced axial elongation in the more myopic eyes and reduced interocular AL differences in children with myopic anisometropia. The refractive state of the untreated eyes did not affect the axial elongation of the more myopic eye wearing the orthokeratology lens. In the untreated eyes, AL increased faster in the low myopia subgroup than in the emmetropia subgroup

The effects of circularly polarized light on mating behavior and gene expression in Anomala corpulenta (Coleoptera: Scarabaeidae)

Light is an important abiotic factor affecting insect behavior. In nature, linearly polarized light is common, but circularly polarized light is rare. Left circularly polarized (LCP) light is selectively reflected by the exocuticle of most scarab beetles, including Anomala corpulenta. Despite our previous research showing that this visual signal probably mediates their mating behavior, the way in which it does so is not well elucidated. In this study, we investigated how LCP light affects not only mating behavior but also gene expression in this species using RNA-seq. The results indicated that disruption of LCP light reflection by females of A. corpulenta probably affects the process by which males of A. corpulenta search for mates. Furthermore, the RNA-seq results showed that genes of the environmental signaling pathways and also of several insect reproduction-related amino acid metabolic pathways were differentially expressed in groups exposed and not exposed to LCP light. This implies that A. corpulenta reproduction is probably regulated by LCP light-induced stress. Herein, the results show that LCP light is probably perceived by males of the species, further mediating their mating behavior. However, this hypothesis needs future verification with additional samples

Percutaneous angioplasty and/or stenting versus aggressive medical therapy in patients with symptomatic intracranial atherosclerotic stenosis: a 1-year follow-up study

BackgroundSymptomatic intracranial atherosclerotic stenosis (sICAS) is one of the common causes of ischemic stroke. However, the treatment of sICAS remains a challenge in the past with unfavorable findings. The purpose of this study was to explore the effect of stenting versus aggressive medical management on preventing recurrent stroke in patients with sICAS.MethodsWe prospectively collected the clinical information of patients with sICAS who underwent percutaneous angioplasty and/or stenting (PTAS) or aggressive medical therapy from March 2020 to February 2022. Propensity score matching (PSM) was employed to ensure well-balanced characteristics of two groups. The primary outcome endpoint was defined as recurrent stroke or transient ischemic attack (TIA) within 1 year.ResultsWe enrolled 207 patients (51 in the PTAS and 156 in the aggressive medical groups) with sICAS. No significant difference was found between PTAS group and aggressive medical group for the risk of stroke or TIA in the same territory beyond 30 days through 6 months (P = 0.570) and beyond 30 days through 1 year (P = 0.739) except for within 30 days (P = 0.003). Furthermore, none showed a significant difference for disabling stroke, death and intracranial hemorrhage within 1 year. These results remain stable after adjustment. After PSM, all the outcomes have no significant difference between these two groups.ConclusionThe PTAS has similar treatment outcomes compared with aggressive medical therapy in patients with sICAS across 1-year follow-up

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve