Planar bilayer lipid membranes(BLMs)are widely used as models for cell membranes in various applications,including drug discovery and ***,the nanometer-thick bilayer structure,assembled through hydrophobic interaction...
详细信息
Planar bilayer lipid membranes(BLMs)are widely used as models for cell membranes in various applications,including drug discovery and ***,the nanometer-thick bilayer structure,assembled through hydrophobic interactions of amphiphilic lipid molecules,makes such BLM systems mechanically and electrically *** this study,we developed a device to reform BLMs using a microair *** device consists of a double well divided by a separator with a microaperture,where a BLM was formed by infusing a lipiddispersed solvent and an aqueous droplet into each well in *** the BLM ruptured,a microair bubble was injected from the bottom of the well to split the merged aqueous droplet at the microaperture,which resulted in the reformation of two lipid monolayers on the split *** bringing the two droplets into contact,a new BLM was *** angled step design was introduced in the BLM device to guide the bubble and ensure the splitting of the merged *** also elucidated the optimal bubble inflow rate for the reproducible BLM *** a 4-channel parallel device,we demonstrated the individual and repeatable reformation of *** approach will aid the development of automated and arrayed BLM systems.
The segregation of waste and recycling is essential for effective waste management. Due to the busy schedule most of the people do not have a time to separate their waste. However, there is a significant issue with th...
详细信息
We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by theoretical considerations, we make use of...
详细信息
We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by theoretical considerations, we make use of weighted policy optimization for off-policy RL and describe the main challenges when learning from animal videos. We propose solutions and test our ideas on a 2D navigation task. We show how the use of animal videos improves performance over RL algorithms that do not leverage such observations.
This paper introduces an experimental method to detect the motion of an electrostatic Micro-Electro-Mechanical systems (MEMS) resonator in aqueous media. The resonator comprises a micro cantilever beam subject to elec...
This paper introduces an experimental method to detect the motion of an electrostatic Micro-Electro-Mechanical systems (MEMS) resonator in aqueous media. The resonator comprises a micro cantilever beam subject to electrostatic actuation through a side electrode. A Finite Element Method (FEM) model of the resonator is developed to determine the in-plane mode shapes and their natural frequencies in order to facilitate the experimental study. The motion of the resonator lead to variations in its capacitance and induce a current. The developed experiments demonstrate that motion-induced current can be measured and analyzed to detect the motion of the resonator's higher-order modes and can be used in chemical sensing.
Lexical simplification (LS) method based on pretrained language models is a straightforward yet powerful approach for generating potential substitutes for a complex word through analysis of its contextual surroundings...
详细信息
Lexical simplification (LS) method based on pretrained language models is a straightforward yet powerful approach for generating potential substitutes for a complex word through analysis of its contextual surroundings. Nonetheless, these methods necessitate distinct pretrained models tailored to diverse languages, often overlooking the imperative task of preserving a sentence’s meaning. In this paper, we propose a novel multilingual LS method via zero-shot paraphrasing (LSPG), as paraphrases provide diversity in word selection while preserving the sentence’s meaning. We regard paraphrasing as a zero-shot translation task within multilingual neural machine translation that supports hundreds of languages. Once the input sentence is channeled into the paraphrasing, we embark on the generation of the substitutes. This endeavor is underpinned by a pioneering decoding strategy that concentrates exclusively on the lexical modifications of the complex word. To utilize the strong capabilities of large language models (LLM), we further introduce a novel approach PromLS that incorporates the results of LSPG to generate heuristic-enhanced context, enabling the LLM to generate diverse candidate substitutions. Experimental results demonstrate that LSPG surpasses BERT-based methods and zero-shot GPT3-based methods significantly in English, Spanish, and Portuguese. We also demonstrate a substantial improvement achieved by PromLS compared to the previous state-of-the-art LLM approach. LS approaches usually assume that complex words and their replacements are individual terms, concentrating on word-for-word substitutions. To tackle the more challenging task of multi-word lexical simplification, including phrase-to-phrase replacements, we extend LSPG and PromLS into MultiLSPG and MultiPromLS. MultiLSPG identifies multi-word expressions matched with their corresponding word counts in specific positions, while MultiPromLS, akin to PromLS, utilizes these candidates to generate a heuristi
A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,w...
详细信息
A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.
Welding is both as art and science and its common use is the jointing of points. Some of the welding procedures used for jointing metal pipes are Tungsten Inert Gas and Gas Metal Arc welding and in common practice pla...
详细信息
ISBN:
(数字)9798350386097
ISBN:
(纸本)9798350386103
Welding is both as art and science and its common use is the jointing of points. Some of the welding procedures used for jointing metal pipes are Tungsten Inert Gas and Gas Metal Arc welding and in common practice plastic pipes are bonded with the use of butt-fusion welding procedure. Since 1970’s conjoint analysis was used as a way of finding out what the preference of consumers. The objective of this study is to determine the combination welding attributes that were most preferred using a conjoint analysis approach. With conjoint analysis, together with, the orthogonal design the preference for welding materials and procedures were analyzed. It showed that pipe material with 29.24% was the most preferred attribute and Non-destructive test as the least preferred with $\mathbf{2. 6 6 \%}$. The result of this study may be utilized in future related reviews and could be applicable in other related Mechanical systems requiring welding works.
Operational flood prevention platforms and systems rely upon the advance notice provided by flood forecasts to formulate efficient measures for flood mitigation. Capturing complex spatial heterogeneity and correlation...
详细信息
Operational flood prevention platforms and systems rely upon the advance notice provided by flood forecasts to formulate efficient measures for flood mitigation. Capturing complex spatial heterogeneity and correlation of hydro-meteorological variables is fundamentally challenging for artificial neural networks. This challenge becomes even more significant when the complexity involved introduces systematic biases and time-lag phenomena in flood forecasts. For the first time, this study proposed a Spatiotemporal Hetero Graph-based Long Short-term Memory (SHG-LSTM) model for multi-step-ahead flood forecasting. The case study focused on the Jianxi basin in China. 25,341 hydro-meteorological data, with a temporal resolution of three hours, collected during flood events were divided into training and test datasets for model construction purpose. The model was fed with 3-h streamflow and precipitation data from 23 gauge stations, covering a time span of the preceding 21 h, for generating flood forecasts at 1 up to 7 horizons. To make a comparative analysis, both LSTM and the Spatiotemporal Graph Convolutional Network (S-GCN) were constructed. This study conducted multiple rounds of model training with varying initial parameters to assess the accuracy, stability, and reliability of the LSTM, S-GCN, and SHG-LSTM models. The results demonstrated that the SHG-LSTM model outperformed LSTM and S-GCN models, with an average reduction in the volume error (VE) of 6.5% and 11.1%, respectively, a decrease in the Mean Absolute Error (MAE) of 6.7% and 8.1%, respectively, and a reduction in the Root Mean Square Error (RMSE) of 5.0% and 12.9%, respectively. Furthermore, the SHG-LSTM model not only could efficiently overcome the under-prediction bottleneck, but also could largely mitigate the time-lag phenomenon in flood forecasts, even during the testing stages. These findings indicate that the proposed SHG-LSTM model can provide a general framework for modelling the spatial heterogeneit
This study contributes to visualizing the complicated interrelationships among the sustainable development goals (SDGs) in Taiwanese cities' resource investment and performance under uncertainties. Taiwan faces bo...
详细信息
Bayesian Networks (BNs) represent conditional probability relations among a set of random variables (nodes) in the form of a directed acyclic graph (DAG), and have found diverse applications in knowledge discovery. We...
详细信息
Bayesian Networks (BNs) represent conditional probability relations among a set of random variables (nodes) in the form of a directed acyclic graph (DAG), and have found diverse applications in knowledge discovery. We study the problem of learning the sparse DAG structure of a BN from continuous observational data. The central problem can be modeled as a mixed-integer program with an objective function composed of a convex quadratic loss function and a regularization penalty subject to linear constraints. The optimal solution to this mathematical program is known to have desirable statistical properties under certain conditions. However, the state-of-the-art optimization solvers are not able to obtain provably optimal solutions to the existing mathematical formulations for mediumsize problems within reasonable computational times. To address this difficulty, we tackle the problem from both computational and statistical perspectives. On the one hand, we propose a concrete early stopping criterion to terminate the branch-and-bound process in order to obtain a near-optimal solution to the mixed-integer program, and establish the consistency of this approximate solution. On the other hand, we improve the existing formulations by replacing the linear "big-M" constraints that represent the relationship between the continuous and binary indicator variables with second-order conic constraints. Our numerical results demonstrate the effectiveness of the proposed approaches.
暂无评论