Electrospinning Technology in Non-Woven Fabric Manufacturing

In the past two decades, research on electrospinning has boomed due to its simple process, small fiber diameter, and special physical and chemical properties. The electrospun fiber is spontaneously collected in a non-woven status in most cases. Therefore, the electrospinning method is becoming an ideal candidate for non-woven fabric manufacturing on a nano scale. More than 50,000 research papers have been published linked to the concept of "electrospinning", and the number is still increasing rapidly. At the early stage of electrospinning research, most of the published papers mainly focused on the research of spinning theories, material systems, and spinning processing. Since then research has turned to functional electrospun fiber preparation and characterization. In recent years, more and more researchers have started to develop a scaling-up method related to the applied products of electrospinning. Interestingly, most electrospinning products are in a non-woven state; that is why we dedicate one chapter to exhibit ongoing, on-woven fabric manufacturing and the basic research progress made using the electrospinning method

Business Analysis and Future Development of an Electric Vehicle Company -- Tesla

The boom in electric vehicles in recent years has caught the attention of many companies that are investing or will be investing in the industry due to the increasing demand for electric cars. Tesla as a leader of the electric vehicles (EVs) industry, its development is of vital significance for referential value. Previous research on electric vehicle acceptance and behavioral intention of purchase is comprehensive, which could enable the EVs industry to understand consumer psychology. However, there is little analysis of the business strategy and future development of specific companies. When it comes to sustainability, almost every company has a path that is best suited to. This paper presents a comprehensive review of the historical background of Tesla, followed by in-depth states on its current strategy and future analysis. Given recommendations on its future development, Tesla could engage more in other different industries to increase the source of revenue and invest more into the development of autonomous public transportation, such as electric car-sharing services (ECS). These will help Tesla move steadily into the next stage

Natural Language based Context Modeling and Reasoning with LLMs: A Tutorial

Large language models (LLMs) have become phenomenally surging, since 2018--two decades after introducing context-awareness into computing systems. Through taking into account the situations of ubiquitous devices, users and the societies, context-aware computing has enabled a wide spectrum of innovative applications, such as assisted living, location-based social network services and so on. To recognize contexts and make decisions for actions accordingly, various artificial intelligence technologies, such as Ontology and OWL, have been adopted as representations for context modeling and reasoning. Recently, with the rise of LLMs and their improved natural language understanding and reasoning capabilities, it has become feasible to model contexts using natural language and perform context reasoning by interacting with LLMs such as ChatGPT and GPT-4. In this tutorial, we demonstrate the use of texts, prompts, and autonomous agents (AutoAgents) that enable LLMs to perform context modeling and reasoning without requiring fine-tuning of the model. We organize and introduce works in the related field, and name this computing paradigm as the LLM-driven Context-aware Computing (LCaC). In the LCaC paradigm, users' requests, sensors reading data, and the command to actuators are supposed to be represented as texts. Given the text of users' request and sensor data, the AutoAgent models the context by prompting and sends to the LLM for context reasoning. LLM generates a plan of actions and responds to the AutoAgent, which later follows the action plan to foster context-awareness. To prove the concepts, we use two showcases--(1) operating a mobile z-arm in an apartment for assisted living, and (2) planning a trip and scheduling the itinerary in a context-aware and personalized manner.Comment: Under revie

EdgeSense: Edge-Mediated Spatial-Temporal Crowdsensing

Edge computing recently is increasingly popular due to the growth of data size and the need of sensing with the reduced center. Based on Edge computing architecture, we propose a novel crowdsensing framework called Edge-Mediated Spatial-Temporal Crowdsensing. This algorithm targets on receiving the environment information such as air pollution, temperature, and traffic flow in some parts of the goal area, and does not aggregate sensor data with its location information. Specifically, EdgeSense works on top of a secured peer-To-peer network consisted of participants and propose a novel Decentralized Spatial-Temporal Crowdsensing framework based on Parallelized Stochastic Gradient Descent. To approximate the sensing data in each part of the target area in each sensing cycle, EdgeSense uses the local sensor data in participants\u27 mobile devices to learn the low-rank characteristic and then recovers the sensing data from it. We evaluate the EdgeSense on the real-world data sets (temperature [1] and PM2.5 [2] data sets), where our algorithm can achieve low error in approximation and also can compete with the baseline algorithm which is designed using centralized and aggregated mechanism

Recent research progress on imbibition system of nanoparticle-surfactant dispersions

Nanotechnology has been increasingly applied in the petroleum industry in recent years. In particular, dispersions consisting of nanoparticles and surfactants have been widely investigated. The imbibition system compounded by nanoparticle and surfactant was found to display a high efficiency in enhancing oil recovery. This paper briefly reviews the factors influencing imbibition efficiency. At the same time, the application and mechanism of the imbibition system of nanoparticle-surfactant dispersion in the field of enhanced oil recovery are introduced. Additionally, the limitations and challenges that the imbibition system of nanoparticle-surfactant dispersions may face in enhanced oil recovery applications are put forward. The current work reveals that the imbibition system with nanoparticle-surfactant dispersion is an ideal candidate for enhanced oil recovery in tight and low-permeability reservoirs.Document Type: Invited reviewCited as: Shao, W., Yang, J., Wang, H., Chang, J., Wu, H., Hou, J. Recent research progress on imbibition system of nanoparticle-surfactant dispersions. Capillarity, 2023, 8(2): 34-44. https://doi.org/10.46690/capi.2023.08.0

One-Step Generation of Mice Carrying Reporter and Conditional Alleles by CRISPR/Cas-Mediated Genome Engineering

The type II bacterial CRISPR/Cas system is a novel genome-engineering technology with the ease of multiplexed gene targeting. Here, we created reporter and conditional mutant mice by coinjection of zygotes with Cas9 mRNA and different guide RNAs (sgRNAs) as well as DNA vectors of different sizes. Using this one-step procedure we generated mice carrying a tag or a fluorescent reporter construct in the Nanog, the Sox2, and the Oct4 gene as well as Mecp2 conditional mutant mice. In addition, using sgRNAs targeting two separate sites in the Mecp2 gene, we produced mice harboring the predicted deletions of about 700 bps. Finally, we analyzed potential off-targets of five sgRNAs in gene-modified mice and ESC lines and identified off-target mutations in only rare instances.United States. National Institutes of Health (HD 045022)United States. National Institutes of Health (R37CA084198

Early Detection of Disease using Electronic Health Records and Fisher\u27s Wishart Discriminant Analysis

Linear Discriminant Analysis (LDA) is a simple and effective technique for pattern classification, while it is also widely-used for early detection of diseases using Electronic Health Records (EHR) data. However, the performance of LDA for EHR data classification is frequently affected by two main factors: ill-posed estimation of LDA parameters (e.g., covariance matrix), and linear inseparability of the EHR data for classification. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fisher\u27s Wishart Discriminant Analysis, which is developed as a faster and robust nonlinear classifier. Specifically, FWDA first surrogates the distribution of potential inverse covariance matrix estimates using a Wishart distribution estimated from the training data. Then, FWDA samples a group of inverse covariance matrices from the Wishart distribution, predicts using LDA classifiers based on the sampled inverse covariance matrices, and weighted-averages the prediction results via Bayesian Voting scheme. The weights for voting are optimally updated to adapt each new input data, so as to enable the nonlinear classification

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares. As the objectives of mobile sensing could be either \emph{(a) personalized medicine for individuals} or \emph{(b) public health for populations}, in this work we review the design of these mobile sensing apps, and propose to categorize the design of these apps/systems in two paradigms -- \emph{(i) Personal Sensing} and \emph{(ii) Crowd Sensing} paradigms. While both sensing paradigms might incorporate with common ubiquitous sensing technologies, such as wearable sensors, mobility monitoring, mobile data offloading, and/or cloud-based data analytics to collect and process sensing data from individuals, we present a novel taxonomy system with two major components that can specify and classify apps/systems from aspects of the life-cycle of mHealth Sensing: \emph{(1) Sensing Task Creation \& Participation}, \emph{(2) Health Surveillance \& Data Collection}, and \emph{(3) Data Analysis \& Knowledge Discovery}. With respect to different goals of the two paradigms, this work systematically reviews this field, and summarizes the design of typical apps/systems in the view of the configurations and interactions between these two components. In addition to summarization, the proposed taxonomy system also helps figure out the potential directions of mobile sensing for health from both personalized medicines and population health perspectives.Comment: Submitted to a journal for revie

SPACE-TA: cost-effective task allocation exploiting intradata and interdata correlations in sparse crowdsensing

Data quality and budget are two primary concerns in urban-scale mobile crowdsensing. Traditional research on mobile crowdsensing mainly takes sensing coverage ratio as the data quality metric rather than the overall sensed data error in the target-sensing area. In this article, we propose to leverage spatiotemporal correlations among the sensed data in the target-sensing area to significantly reduce the number of sensing task assignments. In particular, we exploit both intradata correlations within the same type of sensed data and interdata correlations among different types of sensed data in the sensing task. We propose a novel crowdsensing task allocation framework called SPACE-TA (SPArse Cost-Effective Task Allocation), combining compressive sensing, statistical analysis, active learning, and transfer learning, to dynamically select a small set of subareas for sensing in each timeslot (cycle), while inferring the data of unsensed subareas under a probabilistic data quality guarantee. Evaluations on real-life temperature, humidity, air quality, and traffic monitoring datasets verify the effectiveness of SPACE-TA. In the temperature- monitoring task leveraging intradata correlations, SPACE-TA requires data from only 15.5% of the subareas while keeping the inference error below 0.25°C in 95% of the cycles, reducing the number of sensed subareas by 18.0% to 26.5% compared to baselines. When multiple tasks run simultaneously, for example, for temperature and humidity monitoring, SPACE-TA can further reduce ∼10% of the sensed subareas by exploiting interdata correlations