Robust Parameter Design of Functional Responses Based on Bayesian SUR Models

As for the robust parameter design of functional responses, a Bayesian Seemingly Unrelated Regression (SUR) model is proposed to take into account the model uncertainty and response variability in this paper. First of all, the SUR model is used to build the functional relationship between the output responses and the input factors at different time points. Also, Bayesian analysis of the SUR model is performed to consider the influence of the model parameter uncertainty on the research results. Secondly, the process means and variances of the functional responses at different time points are estimated by the posterior samples of the simulated responses. Moreover, an integrated performance index (i.e. mean square error) is establish by using the above process means and variances. Then, the optimal parameter settings may be found by minimizing the MSE performance index. Finally, the advantages of the proposed method are illustrated by an example from the literature

Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices

Today, deep learning optimization is primarily driven by research focused on achieving high inference accuracy and reducing latency. However, the energy efficiency aspect is often overlooked, possibly due to a lack of sustainability mindset in the field and the absence of a holistic energy dataset. In this paper, we conduct a threefold study, including energy measurement, prediction, and efficiency scoring, with an objective to foster transparency in power and energy consumption within deep learning across various edge devices. Firstly, we present a detailed, first-of-its-kind measurement study that uncovers the energy consumption characteristics of on-device deep learning. This study results in the creation of three extensive energy datasets for edge devices, covering a wide range of kernels, state-of-the-art DNN models, and popular AI applications. Secondly, we design and implement the first kernel-level energy predictors for edge devices based on our kernel-level energy dataset. Evaluation results demonstrate the ability of our predictors to provide consistent and accurate energy estimations on unseen DNN models. Lastly, we introduce two scoring metrics, PCS and IECS, developed to convert complex power and energy consumption data of an edge device into an easily understandable manner for edge device end-users. We hope our work can help shift the mindset of both end-users and the research community towards sustainability in edge computing, a principle that drives our research. Find data, code, and more up-to-date information at https://amai-gsu.github.io/DeepEn2023.Comment: This paper has been accepted by ACM/IEEE Symposium on Edge Computing (SEC '23

Genome sequences reveal global dispersal routes and suggest convergent genetic adaptations in seahorse evolution

Seahorses have a circum-global distribution in tropical to temperate coastal waters. Yet, seahorses show many adaptations for a sedentary, cryptic lifestyle: they require specific habitats, such as seagrass, kelp or coral reefs, lack pelvic and caudal fins, and give birth to directly developed offspring without pronounced pelagic larval stage, rendering long-range dispersal by conventional means inefficient. Here we investigate seahorses’ worldwide dispersal and biogeographic patterns based on a de novo genome assembly of Hippocampus erectus as well as 358 re-sequenced genomes from 21 species. Seahorses evolved in the late Oligocene and subsequent circum-global colonization routes are identified and linked to changing dynamics in ocean currents and paleo-temporal seaway openings. Furthermore, the genetic basis of the recurring “bony spines” adaptive phenotype is linked to independent substitutions in a key developmental gene. Analyses thus suggest that rafting via ocean currents compensates for poor dispersal and rapid adaptation facilitates colonizing new habitats.Fil: Chunyan, Li. Southern Marine Science and Engineering Guangdong Laboratory; China. Pilot National Laboratory for Marine Science and Technology; China. Chinese Academy of Sciences; República de ChinaFil: Olave, Melisa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; Argentina. University of Konstanz; AlemaniaFil: Hou, Yali. Chinese Academy of Sciences; República de ChinaFil: Geng, Qi. Chinese Academy of Sciences; República de China. Southern Marine Science and Engineering Guangdong Laboratory; ChinaFil: Schneider, Ralf. University Of Konstanz; Alemania. Helmholtz Centre for Ocean Research Kie; AlemaniaFil: Zeixa, Gao. Huazhong Agricultural University; ChinaFil: Xiaolong, Tu. Allwegene Technologies ; ChinaFil: Xin, Wang. Chinese Academy of Sciences; República de ChinaFil: Furong, Qi. China National Center for Bioinformation; China. University of Chinese Academy of Sciences; ChinaFil: Nater, Alexander. University of Konstanz; AlemaniaFil: Kautt, Andreas F.. University of Konstanz; Alemania. Harvard University; Estados UnidosFil: Wan, Shiming. Chinese Academy of Sciences; República de ChinaFil: Yanhong, Zhang. Chinese Academy of Sciences; República de ChinaFil: Yali, Liu. Chinese Academy of Sciences; República de ChinaFil: Huixian, Zhang. Chinese Academy of Sciences; República de ChinaFil: Bo, Zhang. Chinese Academy of Sciences; República de ChinaFil: Hao, Zhang. Chinese Academy of Sciences; República de ChinaFil: Meng, Qu ,. Chinese Academy of Sciences; República de ChinaFil: Shuaishuai, Liu. Chinese Academy of Sciences; República de ChinaFil: Zeyu, Chen. Chinese Academy of Sciences; República de China. University of Chinese Academy of Sciences; ChinaFil: Zhong, Jia. Chinese Academy of Sciences; República de ChinaFil: Zhang, He. BGI-Shenzhen; ChinaFil: Meng, Lingfeng. BGI-Shenzhen; ChinaFil: Wang, Kai. Ludong University; ChinaFil: Yin, Jianping. Chinese Academy of Sciences; República de ChinaFil: Huang, Liangmin. Chinese Academy of Sciences; República de China. University of Chinese Academy of Sciences; ChinaFil: Venkatesh, Byrappa. Institute of Molecular and Cell Biology; SingapurFil: Meyer, Axel. University of Konstanz; AlemaniaFil: Lu, Xuemei. Chinese Academy of Sciences; República de ChinaFil: Lin, Qiang. Chinese Academy of Sciences; República de China. Southern Marine Science and Engineering Guangdong Laboratory; China. Pilot National Laboratory for Marine Science and Technology; China. University of Chinese Academy of Sciences; Chin

Fabrication and study of multidimensional scaffolds for cellular and tissue engineering

L'objectif de ce travail est de développer une méthode d'ingénierie de scaffolds multidimensionnels pour la culture cellulaire et l’ingénierie tissulaire. Nous avons d'abord appliqué une technique d'impression 3D pour produire un scaffold en PEGDA et ensuite rempli l'espace libre du scaffold avec du gel de gélatine. Après la congélation et le séchage, un scaffold hybride en PEGDA avec des structures fine de gélatine a été obtenu, qui a été ensuite valisé par la culture et la différenciation des cellules progénitrices neuronales. Pour intégrer plus facilement dans un dispositif microfluidique, nous avons également conçu un scaffold 2D sous forme d’une couche mince de nid d'abeilles de PEGDA rempli des structures poreuses auto-assemblée de PCL. Ce scaffold 2D a été utilisé pour la culture cellulaire et la transfection des gènes, montrant des avantages par rapport aux méthodes classiques en termes d'absorption des nutriments et des facteurs solubles. Enfin, nous avons fabriqué un scaffold mous constitué d’une couche mince de nid d'abeilles en élastomère de PDMS et d’une monocouche de nanofibres de gélatine pour faciliter la différenciation cardiaque à partir des cellules souches pluripotentes humaine. Comme prévu, nous avons réalisé une génération cardiaque avec une contraction plus forte et une homogénéité de battement plus élevée par rapport aux approches classiques. Tous ensemble, nous avons démontré l'utilité des scaffolds hybrides pour l'ingénierie micro-tissulaire qui pourraient avoir un impact sur les études futures dans les domaines de l'ingénierie tissulaire, du criblage des médicaments et de la médecine régénératrice.The objective of this work is to develop a method of engineering multi-dimensional scaffolds for cell culture and tissue formation. We firstly applied a 3D printing technique to produce the designed frame in PEGDA and then filled the free-space of the frame with a gelatin gel. After freezing and drying, a hybrid 3D scaffold made of gelatin porous structures and PEDGA backbone was obtained, which supported culture and differentiation of neural progenitor cells. To more easily integrate into a microfluidic device, we also designed a 2D scaffold in form of a thin layer of honeycomb frame of PEGDA and self-assembled porous structure of PCL. Such a patch form scaffold could be used for cell culture and gene transfection, showing advantages over the conventional methods in terms of nutrients and soluble factors uptake. Finally, we fabricated a soft patch made of an elastic frame in PDMS and a monolayer of gelatin nanofibers to facilitate cardiac differentiation from human induced pluripotent stem cells. As expected, we achieved a cardiac generation with higher contraction strength and a higher beating homogeneity comparing to the conventional approaches. All together, we demonstrated the utility of hybrid scaffolds for micro-tissue engineering which could impact the future studies in the fields of tissue engineering, drug screening and regenerative medicine

Fabrication et étude de scaffolds multidimensionnels pour l'ingénierie cellulaire et tissulaire

The objective of this work is to develop a method of engineering multi-dimensional scaffolds for cell culture and tissue formation. We firstly applied a 3D printing technique to produce the designed frame in PEGDA and then filled the free-space of the frame with a gelatin gel. After freezing and drying, a hybrid 3D scaffold made of gelatin porous structures and PEDGA backbone was obtained, which supported culture and differentiation of neural progenitor cells. To more easily integrate into a microfluidic device, we also designed a 2D scaffold in form of a thin layer of honeycomb frame of PEGDA and self-assembled porous structure of PCL. Such a patch form scaffold could be used for cell culture and gene transfection, showing advantages over the conventional methods in terms of nutrients and soluble factors uptake. Finally, we fabricated a soft patch made of an elastic frame in PDMS and a monolayer of gelatin nanofibers to facilitate cardiac differentiation from human induced pluripotent stem cells. As expected, we achieved a cardiac generation with higher contraction strength and a higher beating homogeneity comparing to the conventional approaches. All together, we demonstrated the utility of hybrid scaffolds for micro-tissue engineering which could impact the future studies in the fields of tissue engineering, drug screening and regenerative medicine.L'objectif de ce travail est de développer une méthode d'ingénierie de scaffolds multidimensionnels pour la culture cellulaire et l’ingénierie tissulaire. Nous avons d'abord appliqué une technique d'impression 3D pour produire un scaffold en PEGDA et ensuite rempli l'espace libre du scaffold avec du gel de gélatine. Après la congélation et le séchage, un scaffold hybride en PEGDA avec des structures fine de gélatine a été obtenu, qui a été ensuite valisé par la culture et la différenciation des cellules progénitrices neuronales. Pour intégrer plus facilement dans un dispositif microfluidique, nous avons également conçu un scaffold 2D sous forme d’une couche mince de nid d'abeilles de PEGDA rempli des structures poreuses auto-assemblée de PCL. Ce scaffold 2D a été utilisé pour la culture cellulaire et la transfection des gènes, montrant des avantages par rapport aux méthodes classiques en termes d'absorption des nutriments et des facteurs solubles. Enfin, nous avons fabriqué un scaffold mous constitué d’une couche mince de nid d'abeilles en élastomère de PDMS et d’une monocouche de nanofibres de gélatine pour faciliter la différenciation cardiaque à partir des cellules souches pluripotentes humaine. Comme prévu, nous avons réalisé une génération cardiaque avec une contraction plus forte et une homogénéité de battement plus élevée par rapport aux approches classiques. Tous ensemble, nous avons démontré l'utilité des scaffolds hybrides pour l'ingénierie micro-tissulaire qui pourraient avoir un impact sur les études futures dans les domaines de l'ingénierie tissulaire, du criblage des médicaments et de la médecine régénératrice

Preparation of LiFePO4 Powders by Ultrasonic Spray Drying Method and Their Memory Effect

The memory effect of lithium-ion batteries (LIBs) was first discovered in LiFePO4, but its origin and dependence are still not clear, which is essential for regulating the memory effect. In this paper, a home-made spray drying device was used to successfully synthesize LiFePO4 with an average particle size of about 1 μm, and we studied the influence of spray drying temperature on the memory effect of LiFePO4 in LIBs. The results showed that the increasing of spray drying temperature made the memory effect of LiFePO4 strengthen from 1.3 mV to 2.9 mV, while the capacity decreased by approximately 6%. The XRD refinement and FTIR spectra indicate that the enhancement of memory effect can be attributed to the increment of Li–Fe dislocations. This work reveals the dependence of memory effect of LiFePO4 on spray drying temperature, which will guide us to optimize the preparation process of electrode materials and improve the management system of LIBs

Survival analysis following dynamic randomization

In this paper, we propose a method to analyze survival data from a clinical trial that utilizes a dynamic randomization for subject enrollment. The method directly accounts for dynamic subject randomization process using a marked point process (MPP). Its corresponding martingale process is used to formulate an equation for estimating the treatment effect size and for hypothesis testing. We perform simulation analyses to evaluate the outcomes of the proposed method as well as the conventional log rank method and re-randomized testing procedure