National Pingtung University

About: National Pingtung University is a education organization based out in Pingtung City, Taiwan. It is known for research contribution in the topics: Population & Supply chain. The organization has 751 authors who have published 813 publications receiving 7655 citations.

A Deep CNN-LSTM Model for Particulate Matter (PM 2.5 ) Forecasting in Smart Cities.

Abstract: In modern society, air pollution is an important topic as this pollution exerts a critically bad influence on human health and the environment. Among air pollutants, Particulate Matter (PM2.5) consists of suspended particles with a diameter equal to or less than 2.5 μm. Sources of PM2.5 can be coal-fired power generation, smoke, or dusts. These suspended particles in the air can damage the respiratory and cardiovascular systems of the human body, which may further lead to other diseases such as asthma, lung cancer, or cardiovascular diseases. To monitor and estimate the PM2.5 concentration, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined and applied to the PM2.5 forecasting system. To compare the overall performance of each algorithm, four measurement indexes, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) Pearson correlation coefficient and Index of Agreement (IA) are applied to the experiments in this paper. Compared with other machine learning methods, the experimental results showed that the forecasting accuracy of the proposed CNN-LSTM model (APNet) is verified to be the highest in this paper. For the CNN-LSTM model, its feasibility and practicability to forecast the PM2.5 concentration are also verified in this paper. The main contribution of this paper is to develop a deep neural network model that integrates the CNN and LSTM architectures, and through historical data such as cumulated hours of rain, cumulated wind speed and PM2.5 concentration. In the future, this study can also be applied to the prevention and control of PM2.5.

Green Innovation and Performance: The View of Organizational Capability and Social Reciprocity

Abstract: Synthesizing insights from a dynamic capability perspective and social network theory, this study identifies the factors influencing green innovation and examines the relationships between influencing factors, green innovation, and performance. This study uses structural equation modeling to test the research hypotheses. The results indicate that dynamic capability, coordination capability, and social reciprocity are significant drivers of green innovation, including green product innovation and green process innovation. Green product and process innovation have positive effects on environmental performance and organizational performance. These findings are relevant to firms in quest of green management and innovation.

The role of organizational culture in the knowledge management process

Abstract: Purpose – The purpose of the study is to focus on the enhancement of knowledge management (KM) performance and the relationship between organizational culture and KM process intention of individuals because of the diversity of organizational cultures (which include results-oriented, tightly controlled, job-oriented, closed system and professional-oriented cultures) Knowledge is a primary resource in organizations If firms are able to effectively manage their knowledge resources, then a wide range of benefits can be reaped such as improved corporate efficiency, effectiveness, innovation and customer service Design/methodology/approach – The survey methodology, which has the ability to enhance generalization of results (Dooley, 2001), was used to collect the data utilized in the testing of the research hypotheses Findings – Results- and job-oriented cultures have positive effects on employee intention in the KM process (creation, storage, transfer and application), whereas a tightly controlled culture h

Potential Toxicity of Iron Oxide Magnetic Nanoparticles: A Review.

Abstract: The noteworthy intensification in the development of nanotechnology has led to the development of various types of nanoparticles. The diverse applications of these nanoparticles make them desirable candidate for areas such as drug delivery, coasmetics, medicine, electronics, and contrast agents for magnetic resonance imaging (MRI) and so on. Iron oxide magnetic nanoparticles are a branch of nanoparticles which is specifically being considered as a contrast agent for MRI as well as targeted drug delivery vehicles, angiogenic therapy and chemotherapy as small size gives them advantage to travel intravascular or intracavity actively for drug delivery. Besides the mentioned advantages, the toxicity of the iron oxide magnetic nanoparticles is still less explored. For in vivo applications magnetic nanoparticles should be nontoxic and compatible with the body fluids. These particles tend to degrade in the body hence there is a need to understand the toxicity of the particles as whole and degraded products interacting within the body. Some nanoparticles have demonstrated toxic effects such inflammation, ulceration, and decreases in growth rate, decline in viability and triggering of neurobehavioral alterations in plants and cell lines as well as in animal models. The cause of nanoparticles' toxicity is attributed to their specific characteristics of great surface to volume ratio, chemical composition, size, and dosage, retention in body, immunogenicity, organ specific toxicity, breakdown and elimination from the body. In the current review paper, we aim to sum up the current knowledge on the toxic effects of different magnetic nanoparticles on cell lines, marine organisms and rodents. We believe that the comprehensive data can provide significant study parameters and recent developments in the field. Thereafter, collecting profound knowledge on the background of the subject matter, will contribute to drive research in this field in a new sustainable direction.