136 research outputs found
Sex Ratio and Sexual Size Dimorphism in a Toad-headed Lizard, Phrynocephalus guinanensis
Phrynocephalus guinanensis has sexual dimorphism in abdominal coloration, but its ontogenetic development of sexual size dimorphism (SSD) is unknown. Using mark-recapture data during four days each year from August from 2014 to 2016, we investigated the development of sex ratios, SSD, sex-specific survivorship and growth rates in a population of P. guinanensis. Our results indicated that the sex ratio of males to females was 1:2.8. Males had a lower survival rate (6%) than females (14%) across the age range from hatchling to adult, which supported the discovered female-biased sex ratio potentially associated with the low survival rate of males between hatchlings and juveniles. Male-biased SSD in tail length and head width existed in adults rather than in hatchling or juvenile lizards. The growth rates in body dimensions were undistinguishable between the sexes during the age from hatchling to juvenile, but the growth rate in head length from juvenile to adult was significantly larger in males than females. Average growth rate of all morphological measurements from hatchling to juvenile were larger compared with corresponding measurements from juvenile to adult, but only being significant in tail length, head width, abdomen length in females and snout-vent length in males. We provided a case study to strengthen our understanding of the important life history traits on how a viviparous lizard population can survive and develop their morphology in cold climates
An advanced Lithium-ion battery optimal charging strategy based on a coupled thermoelectric model
Lithium-ion batteries are widely adopted as the power supplies for electric vehicles. A key but challenging issue is to achieve optimal battery charging, while taking into account of various constraints for safe, efficient and reliable operation. In this paper, a triple-objective function is first formulated for battery charging based on a coupled thermoelectric model. An advanced optimal charging strategy is then proposed to develop the optimal constant-current-constant-voltage (CCCV) charge current profile, which gives the best trade-off among three conflicting but important objectives for battery management. To be specific, a coupled thermoelectric battery model is first presented. Then, a specific triple-objective function consisting of three objectives, namely charging time, energy loss, and temperature rise (both the interior and surface), is proposed. Heuristic methods such as Teaching-learning-based-optimization (TLBO) and particle swarm optimization (PSO) are applied to optimize the triple-objective function, and their optimization performances are compared. The impacts of the weights for different terms in the objective function are then assessed. Experimental results show that the proposed optimal charging strategy is capable of offering desirable effective optimal charging current profiles and a proper trade-off among the conflicting objectives. Further, the proposed optimal charging strategy can be easily extended to other battery types
Prompt Injection attack against LLM-integrated Applications
Large Language Models (LLMs), renowned for their superior proficiency in
language comprehension and generation, stimulate a vibrant ecosystem of
applications around them. However, their extensive assimilation into various
services introduces significant security risks. This study deconstructs the
complexities and implications of prompt injection attacks on actual
LLM-integrated applications. Initially, we conduct an exploratory analysis on
ten commercial applications, highlighting the constraints of current attack
strategies in practice. Prompted by these limitations, we subsequently
formulate HouYi, a novel black-box prompt injection attack technique, which
draws inspiration from traditional web injection attacks. HouYi is
compartmentalized into three crucial elements: a seamlessly-incorporated
pre-constructed prompt, an injection prompt inducing context partition, and a
malicious payload designed to fulfill the attack objectives. Leveraging HouYi,
we unveil previously unknown and severe attack outcomes, such as unrestricted
arbitrary LLM usage and uncomplicated application prompt theft. We deploy HouYi
on 36 actual LLM-integrated applications and discern 31 applications
susceptible to prompt injection. 10 vendors have validated our discoveries,
including Notion, which has the potential to impact millions of users. Our
investigation illuminates both the possible risks of prompt injection attacks
and the possible tactics for mitigation
Automatic interictal epileptiform discharge (IED) detection based on convolutional neural network (CNN)
Clinical diagnosis of epilepsy significantly relies on identifying interictal epileptiform discharge (IED) in electroencephalogram (EEG). IED is generally interpreted manually, and the related process is very time-consuming. Meanwhile, the process is expert-biased, which can easily lead to missed diagnosis and misdiagnosis. In recent years, with the development of deep learning, related algorithms have been used in automatic EEG analysis, but there are still few attempts in IED detection. This study uses the currently most popular convolutional neural network (CNN) framework for EEG analysis for automatic IED detection. The research topic is transferred into a 4-labels classification problem. The algorithm is validated on the long-term EEG of 11 pediatric patients with epilepsy. The computational results confirm that the CNN-based model can obtain high classification accuracy, up to 87%. The study may provide a reference for the future application of deep learning in automatic IED detection
Online identification of lithium-ion battery model parameters with initial value uncertainty and measurement noise
Online parameter identification is essential for the accuracy of the battery equivalent circuit model (ECM). The traditional recursive least squares (RLS) method is easily biased with the noise disturbances from sensors, which degrades the modeling accuracy in practice. Meanwhile, the recursive total least squares (RTLS) method can deal with the noise interferences, but the parameter slowly converges to the reference with initial value uncertainty. To alleviate the above issues, this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM. RLS converges quickly by updating the parameters along the gradient of the cost function. RTLS is applied to attenuate the noise effect once the parameters have converged. Both simulation and experimental results prove that the proposed method has good accuracy, a fast convergence rate, and also robustness against noise corruption
Constrained generalized predictive control of battery charging process based on a coupled thermoelectric model
Battery temperature is a primary factor affecting the battery performance, and suitable battery temperature control in particular internal temperature control can not only guarantee battery safety but also improve its efficiency. This is however challenging as current controller designs for battery charging have no mechanisms to incorporate such information. This paper proposes a novel battery charging control strategy which applies the constrained generalized predictive control (GPC) to charge a LiFePOâ battery based on a newly developed coupled thermoelectric model. The control target primarily aims to maintain the battery cell internal temperature within a desirable range while delivering fast charging. To achieve this, the coupled thermoelectric model is firstly introduced to capture the battery behaviours in particular SOC and internal temperature which are not directly measurable in practice. Then a controlled auto-regressive integrated moving average (CARIMA) model whose parameters are identified by the recursive least squares (RLS) algorithm is developed as an online self-tuning predictive model for a GPC controller. Then the constrained generalized predictive controller is developed to control the charging current. Experiment results confirm the effectiveness of the proposed control strategy. Further, the best region of heat dissipation rate and proper internal temperature set-points are also investigated and analysed
Simultaneous dual-gas QEPAS detection based on a fundamental and overtone combined vibration of quartz tuning fork
A dual-gas quartz-enhanced photoacoustic spectroscopy (QEPAS) sensor system based on a frequency division multiplexing technique of a quartz tuning fork (QTF) was developed and experimentally demonstrated. Two beams from two independently modulated lasers are focused at two different positions between the QTF prongs to excite both the QTF fundamental and 1st overtone flexural modes simultaneously. The 2f-wavelength modulation technique is employed by applying two sinusoidal dithers, whose frequencies are equal to a half of the QTF fundamental and 1st overtone frequencies, respectively, to the currents of two excitation lasers. The resonance frequency difference between two flexural modes ensures that the correlated photoacoustic signals generated by different target gases do not interfere with each other. The proposed QEPAS methodology realizes a continuous real-time dual-gas monitoring with a simple setup and small sensor size compared with previous multi-gas QEPAS sensors
The enormous repetitive Antarctic krill genome reveals environmental adaptations and population insights
Antarctic krill (Euphausia superba) is Earthâsmost abundant wild animal, and its enormous biomass is vital to
the Southern Ocean ecosystem. Here, we report a 48.01-Gb chromosome-level Antarctic krill genome, whose
large genome size appears to have resulted from inter-genic transposable element expansions. Our assembly
reveals the molecular architecture of the Antarctic krill circadian clock and uncovers expanded gene
families associated with molting and energy metabolism, providing insights into adaptations to the cold
and highly seasonal Antarctic environment. Population-level genome re-sequencing from four geographical
sites around the Antarctic continent reveals no clear population structure but highlights natural selection
associated with environmental variables. An apparent drastic reduction in krill population size 10 mya and
a subsequent rebound 100 thousand years ago coincides with climate change events. Our findings uncover
the genomic basis of Antarctic krill adaptations to the Southern Ocean and provide valuable resources for
future Antarctic research
Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.
Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.This work is part of the ââSpatioTemporal Omics Consortiumââ (STOC) paper package. A list of STOC members is available at: http://sto-consortium.org. We would
like to thank the MOTIC China Group, Rongqin Ke (Huaqiao University, Xiamen,
China), Jiazuan Ni (Shenzhen University, Shenzhen, China), Wei Huang (Center
for Excellence in Brain Science and Intelligence Technology, Chinese Academy
of Sciences, Shanghai, China), and Jonathan S. Weissman (Whitehead Institute,
Boston, USA) for their help. This work was supported by the grant of Top Ten
Foundamental Research Institutes of Shenzhen, the Shenzhen Key Laboratory
of Single-Cell Omics (ZDSYS20190902093613831), and the Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011); Longqi Liu
was supported by the National Natural Science Foundation of China
(31900466) and Miguel A. Estebanâs laboratory at the Guangzhou Institutes of
Biomedicine and Health by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), National Natural Science Foundation of China (92068106), and the Guangdong Basic and Applied Basic Research
Foundation (2021B1515120075).S
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