In the field of polymer‐based photovoltaic cells, poly(3‐hexylthiophene) (P3HT) and 1‐(3‐methoxycarbonyl)propyl‐1‐phenyl6,6C61 (PCBM) are, to date, the most‐studied active materials around the world ...for the bulk‐heterojunction structure. Various power‐conversion efficiencies are reported up to approximately 5%. This Research News article is focused on a survey of the tremendous literature published between 2002 and 2010 that exhibits solar cells based on blends of P3HT and PCBM.
This Research News article is a survey of the tremendous amount of literature published from 2002 to 2010 that exhibits polymeric solar cells based on the well‐known bulk heterojunction based on P3HT and PCBM. The power‐conversion‐efficiency discrepancy is correlated with the solar‐cell‐fabrication parameters.
The fast development of the Internet of Things (IoT) technology in recent years has supported connections of numerous smart things along with sensors and established seamless data exchange between ...them, so it leads to a stringy requirement for data analysis and data storage platform such as cloud computing and fog computing. Healthcare is one of the application domains in IoT that draws enormous interest from industry, the research community, and the public sector. The development of IoT and cloud computing is improving patient safety, staff satisfaction, and operational efficiency in the medical industry. This survey is conducted to analyze the latest IoT components, applications, and market trends of IoT in healthcare, as well as study current development in IoT and cloud computing-based healthcare applications since 2015. We also consider how promising technologies such as cloud computing, ambient assisted living, big data, and wearables are being applied in the healthcare industry and discover various IoT, e-health regulations and policies worldwide to determine how they assist the sustainable development of IoT and cloud computing in the healthcare industry. Moreover, an in-depth review of IoT privacy and security issues, including potential threats, attack types, and security setups from a healthcare viewpoint is conducted. Finally, this paper analyzes previous well-known security models to deal with security risks and provides trends, highlighted opportunities, and challenges for the IoT-based healthcare future development.
•A manually collected and validated crop pests dataset (10 classes/5629images).•A fine-tuned GoogLeNet model for the identification of 10 crop pests types.•Combine several pre-processing techniques ...to deal with the complex background.•The average detection accuracy for the proposed model is over 98%.•The fine-tuned GoogLeNet model obtained an improvement of 6.22% compared to the state-of-the-art method.
Crop diseases and insect pests are major agricultural problems worldwide, because the severity and extent of their occurrence causes significant crop losses. In addition, traditional crop pests recognition methods are limited, ineffective, and time-consuming due to the manual selection of the useful feature sets. This paper introduces a crop pest recognition method that accurately recognizes ten common species of crop pests by applying several deep convolutional neural networks (CNNs). The main contributions of this paper are (1) a manually collected and validated crop pest dataset is described and shared; (2) a fine-tuned GoogLeNet model is proposed to deal with the complicated backgrounds presented by farmland scenes, with pest classification results better than the original model; and (3) the fine-tuned GoogLeNet model obtains an improvement of 6.22% compared to the state-of-the-art method. As a result, the proposed model has the potential to be applied in real-world applications and further motivate research on crop disease identification.
Image manipulation of the human face is a trending topic of image forgery, which is done by transforming or altering face regions using a set of techniques to accomplish desired outputs. Manipulated ...face images are spreading on the internet due to the rise of social media, causing various societal threats. It is challenging to detect the manipulated face images effectively because (i) there has been a limited number of manipulated face datasets because most datasets contained images generated by GAN models; (ii) previous studies have mainly extracted handcrafted features and fed them into machine learning algorithms to perform manipulated face detection, which was complicated, error-prone, and laborious; and (iii) previous models failed to prove why their model achieved good performances. In order to address these issues, this study introduces a large face manipulation dataset containing vast variations of manipulated images created and manually validated using various manipulation techniques. The dataset is then used to train a fine-tuned RegNet model to detect manipulated face images robustly and efficiently. Finally, a manipulated region analysis technique is implemented to provide some in-depth insights into the manipulated regions. The experimental results revealed that the RegNet model showed the highest classification accuracy of 89% on the proposed dataset compared to standard deep learning models.
•TPS/CTS films were prepared through blown film extrusion.•SEM, CLSM and XPS confirmed the distribution and deposition of chitosan on the outer film surfaces.•A thin chitosan-rich layer on the film ...surfaces caused improved barrier properties and reduced hydrophilicity.•Water vapor and oxygen permeability of TPS/CTS films decreased with increasing chitosan concentration.•TPS/CTS film could potentially be used as an edible film for food and pharmaceutical applications.
Fabrication of starch-based edible film using blown film extrusion is challenging and interesting because this process provides continuous operation with shorter production time and lower energy consumption, is less labor intensive, and results in higher productivity than the conventional solution casting technique. Previously, we reported on the preparation and some properties of thermoplastic starch/chitosan (TPS/CTS) blown films; however, their morphological characteristics and barrier properties had not yet been elucidated. The present work thus aims to investigate the effect of chitosan (0.37–1.45%) on morphological characteristics, water vapor and oxygen barrier properties as well as hydrophilicity of the TPS and TPS/CTS films. The relationship between morphological characteristics and properties of the films was also discussed. Scanning electron microscopy (SEM), confocal laser scanning microscopy (CLSM) and X-ray photoelectron spectroscopy (XPS) confirmed the distribution and deposition of chitosan on the film surface. The existence of chitosan on the surface imparted the improved water vapor and oxygen barrier properties and the reduced surface hydrophilicity to the film. The results suggest that this biodegradable bio-based TPS/CTS film could potentially be used as an edible film for food and pharmaceutical applications.
With the rapid rise of private vehicles around the world, License Plate Recognition (LPR) plays a vital role in supporting the government to manage vehicles effectively. However, an introduction of ...new types of license plate (LP) or slight changes in the LP format can break previous LPR systems, as they fail to recognize the LP. Moreover, the LPR system is extremely sensitive to the conditions of the surrounding environment. Thus, this paper introduces a novel deep learning-based Korean LPR system that can effectively deal with existing challenges. The main contributions of this study include (1) a robust LPR system with the integration of three pre-processing techniques (defogging, low-light enhancement, and super-resolution) that can effectively recognize the LP under various conditions, (2) the establishment of two original Korean LPR approaches for different scenarios, including whole license plate recognition (W-LPR) and single-character license plate recognition (SC-LPR), and (3) the introduction of two Korean LPR datasets (synthetic data and real data) involving a new type of LP introduced by the Korean government. Through several experiments, the proposed LPR framework achieved the highest recognition accuracy of 98.94%.
Thanks to the exponential growth in computing power and vast amounts of data, artificial intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be ubiquitously ...adopted in our daily lives. Even though AI-powered systems have brought competitive advantages, the black-box nature makes them lack transparency and prevents them from explaining their decisions. This issue has motivated the introduction of explainable artificial intelligence (XAI), which promotes AI algorithms that can show their internal process and explain how they made decisions. The number of XAI research has increased significantly in recent years, but there lacks a unified and comprehensive review of the latest XAI progress. This review aims to bridge the gap by discovering the critical perspectives of the rapidly growing body of research associated with XAI. After offering the readers a solid XAI background, we analyze and review various XAI methods, which are grouped into (i) pre-modeling explainability, (ii) interpretable model, and (iii) post-modeling explainability. We also pay attention to the current methods that dedicate to interpret and analyze deep learning methods. In addition, we systematically discuss various XAI challenges, such as the trade-off between the performance and the explainability, evaluation methods, security, and policy. Finally, we show the standard approaches that are leveraged to deal with the mentioned challenges.
For more than 15 years, TPX2 has been studied as a factor critical for mitosis and spindle assembly. These functions of TPX2 are attributed to its Ran-regulated microtubule-associated protein ...properties and to its control of the Aurora A kinase. Overexpressed in cancers, TPX2 is being established as marker for the diagnosis and prognosis of malignancies. During interphase, TPX2 resides preferentially in the nucleus where its function had remained elusive until recently. The latest finding that TPX2 plays a role in amplification of the DNA damage response, combined with the characterization of TPX2 knockout mice, open new perspectives to understand the biology of this protein. This review provides an historic overview of the discovery of TPX2 and summarizes its cytoskeletal and signaling roles with relevance to cancer therapies. Finally, the review aims to reconcile discrepancies between the experimental and pathological effects of TPX2 overexpression and advances new roles for compartmentalized TPX2.
Financial news has been proven to be a crucial factor which causes fluctuations in stock prices. However, previous studies heavily relied on analyzing shallow features and ignored the structural ...relation among words in a sentence. Several sentiment analysis studies have tried to point out the relationship between investors' reaction and news events. However, the sentiment dataset was usually constructed from the lingual dataset which is unrelated to the financial sector and led to poor performance. This paper proposes a novel framework to predict the directions of stock prices by using both financial news and sentiment dictionary. The original contributions of this paper include the proposal of a novel two-stream gated recurrent unit network and Stock2Vec-a sentiment word embedding trained on financial news dataset and Harvard IV-4. Two main experiments are conducted: the first experiment predicts S&P 500 index stock price directions using the historical S&P 500 prices and the articles crawled from Reuters and Bloomberg, and the second experiment forecasts the price trends of VN-index using VietStock news and stock prices from cophieu68. Results show that: 1) two-stream GRU outperforms state-of-the-art models; 2) Stock2Vec is more efficient in dealing with financial datasets; and 3) applying the model, a simulation scenario proves that our model is effective for the stock sector.
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