Statistical Technology Analysis for Competitive Sustainability of Three Dimensional Printing

Patent Keyword Analysis of Disaster Artificial Intelligence Using Bayesian Network Modeling and Factor Analysis

At present, artificial intelligence (AI) contributes to most technological fields. AI has also been introduced in the disaster area to replace humans and contribute to the prevention of disasters and the minimization of damages. So, it is necessary to analyze disaster AI in order to effectively make use of it. In this paper, we analyze the patent documents related to disaster AI technology. We propose Bayesian network modeling and factor analysis for the technology analysis of disaster AI. This is based on probability distribution and graph theory. It is also a statistical model that depends on multivariate data analysis. In order to show how the proposed model can be applied to a real problem, we carried out a case study to collect and analyze the patent data related to disaster AI.

Thermal Extrusion 3D Printing for the Fabrication of Puerarin Immediate-Release Tablets

Thermal extrusion (TE) 3D printing is a thermoplastic semisolid-based rapid prototyping process, which is capable of building complex structures. The aim of this study was to manufacture rapid-release puerarin tablets without solvent through TE 3D printing. Novel rapid-release tablets were fabricated with polyethylene glycol (PEG 4000) as the carrier at appropriate puerarin/PEG 4000 ratios, assessed through differential scanning calorimetry (DSC), solubility, and dissolution tests. The novel structures of 3D-printed tablets with five different values were formed by printing paths, which established a flexible way of adjusting in vitro drug release. An obvious acceleration (85% of cumulative release about 7.5 min at the soonest) was observed for the tablets with internal structural design. It was inferred that puerarin formed simple eutectic mixtures with PEG 4000 and that puerarin dispersed into the carrier based on DSC and X-Ray powder diffraction (XRD). This highlights the combined advantage of PEG as a soluble polymer with TE 3D printing and provides a suitable system for rapid puerarin release.

Bayesian Structural Time Series and Regression Modeling for Sustainable Technology Management

Many companies take the sustainability of their technologies very seriously, because companies with sustainable technologies are better able to survive in the market. Thus, sustainable technology analysis is important issue in management of technology (MOT). In this paper, we study the management of sustainable technology (MOST). This focuses on the sustainable technology in various MOT fields. In the MOST, sustainable technology analysis is dependent on time periods. We propose a method of sustainable technology analysis using a Bayesian structural time series (BSTS) model based on time series data. In addition, we use the Bayesian regression to find the relational structure between technologies. To show the performance of our method and how the method can be applied to practical works, we carry out a case study using the patent data related to artificial intelligence technologies.

Sustainable Technology Analysis Using Data Envelopment Analysis and State Space Models

To find sustainable technology in various areas, we propose an analytical methodology based on data envelopment analysis (DEA) and the state space model (SSM). DEA is an analytical method used to compare the efficiencies and performances of several items. In DEA, for sustainable technology analysis, the items of DEA can be the technological keywords or international patent classification (IPC) codes in patent documents. In this paper, the proposed method is used to find the relative performance of different patent keywords using comparison and evaluation. We apply this methodology to compare the technological efficiencies between patent keywords for sustainable technology analysis. We apply the additive model and directional distance function of DEA to develop the proposed methodology for building the technological structure of target technology. In addition, we forecast the future trend of target technology using the SSM and find the area of sustainable technology by its result. The SSM is well suited for time series forecasting on technology analysis. We extract the IPC codes from patent documents for the SSM. In our research, we combine the results of DEA and the SSM to find the area of technological sustainability. To illustrate the validity and performance of our research, we conduct a case study using the patent documents used and registered by Apple.

Enhancing Bank Loyalty through Sustainable Banking Practices: The Mediating Effect of Corporate Image

As the demand for a more sustainable society increases, adopting a sustainable banking approach serves as a competitive advantage for banks that are focused on attaining bank loyalty. This study revolves around understanding the role of sustainable banking practices in bank loyalty while exploring the mediating effect of corporate image on the relationship between sustainable banking practices and bank loyalty. For this study, 511 questionnaires derived from customers of the banking sector were adopted. Results from structural equation modeling showed that sustainable banking practices positively and directly affected bank loyalty and corporate image, corporate image directly and positively affected bank loyalty, and corporate image also mediated the relationship between sustainable banking practices and bank loyalty.

Industry 4.0 and Sustainability Implications: A Scenario-Based Analysis of the Impacts and Challenges

The new evolution of the production and industrial process called Industry 4.0, and its related technologies such as the Internet of Things, big data analytics, and cyber–physical systems, among others, still have an unknown potential impact on sustainability and the environment. In this paper, we conduct a literature-based analysis to discuss the sustainability impact and challenges of Industry 4.0 from four different scenarios: deployment, operation and technologies, integration and compliance with the sustainable development goals, and long-run scenarios. From these scenarios, our analysis resulted in positive or negative impacts related to the basic production inputs and outputs flows: raw material, energy and information consumption and product and waste disposal. As the main results, we identified both positive and negative expected impacts, with some predominance of positives that can be considered positive secondary effects derived from Industry 4.0 activities. However, only through integrating Industry 4.0 with the sustainable development goals in an eco-innovation platform, can it really ensure environmental performance. It is expected that this work can contribute to helping stakeholders, practitioners and governments to advance solutions to deal with the outcomes emerging through the massive adoption of those technologies, as well as supporting the expected positive impacts through policies and financial initiatives.

Bayesian Count Data Modeling for Finding Technological Sustainability

Technology developments change society, and society demands new and innovative technology developments. We analyze technology to understand society and technology itself. Much research related to technology analysis has been introduced in various fields. Most of it has been on patent analysis. This is because detailed and accurate results of research and development are patented. In this paper, we study a new patent analysis method based on the count data model and Bayesian regression analysis. Using the count data model, we analyzed the technological keywords extracted from the collected patent documents. We used the prior distribution of Bayesian statistics to reflect the experience and knowledge of the relevant technological experts in the analysis model. Moreover, we applied the proposed model to find sustainable technologies. Finding and developing sustainable technologies is an important activity for companies and research institutes to maintain their technological competitiveness. To illustrate how our modeling could be applied to real domains, we carried out a case study using the patent documents related to artificial intelligence.