Classification is a kernel process in the standardization, grading, and sensory aspects of coffee industries. The chemometric data of fatty acids and crude fat are used to characterize the varieties of coffee. Two category classifiers were used to distinguish the species and roasting degree of coffee beans. However, the fatty acid profiling with normalized data gave a bad discriminant result in the classification study with mixed dimensions in species and roasted degree. The result of the predictive model is in conflict with the context of human cognition, since roasted coffee beans are easily visually distinguished from green coffee beans. By exploring the effects of error analysis and information processing technologies, the lost information was identified as a bias–variance tradeoff derived from the percentile normalization. The roasting degree as extensive information was attenuated by the percentile normalization, but the cultivars as intensive information were enhanced. An informational spiking technique is proposed to patch the dataset and block the information loss. The identified blocking of informational loss could be available for multidimensional classification systems based on the chemometric data.

The research studies constituents of fatty acids (FA) in coffee beans to identify their categories. Since fatty acids are the fundamental constituents of coffee aroma and flavor, challenges occur to classify the beans' original species in the roasted state. The examined samples in this study cover 74 coffee beans from different origins and are separated into Arabica and Robusta species based on their fatty acid composition. This research develops a discriminant strategy to identify categories of examined coffee beans. This study analyzes an experimental dataset using multiple data structure strategies during the identification process, which are different from traditional approaches that aim to improve coffee bean species classification and recognition rate. Furthermore, the developed coffee bean identification strategies implement various normalization and error analyses during the data reasoning process. This research concludes that fatty acids C18:1, C18:2, and C18:3 own essential characteristics for the coffee beans. Practical applicationsThis research develops an innovative strategy to identify coffee beans in well-known categories and implements various normalization and error analyses during the data processing process. The developed model solves the information loss problem due to data normalization and improves the accuracy of coffee been classification.Consequently, the study concludes that fatty acids C18:1, C18:2, and C18:3 characterize essential features for coffee beans, and the research results balance fundamental chemistry and engineering principles and serve the general food processing and preservation technology industries.

Due to global changes, the international community is paying attention to the application of innovative energy technologies to meet the sustainable development of ecology and the environment. As a result, the concept of “waste-to-energy” has been developed. This study proposes a modular device for low-temperature pyrolysis (less than 300 °C) of polymers as a verifiable framework for a decentralized energy supply. Experiments with various plastics as waste feedstocks for conversion into fuel products were carefully analyzed. Mixed plastics (petrochemical polymers) and natural materials (organic polymers) were further subjected to energy conversion efficiency evaluation. The feasibility of continuous implementation was verified, converting 4000 kg of waste plastics with chemical potential into 3188 L of waste polymer oil (WPO), and generating 6031 kWh of electricity. Integrated electromechanical control realizes a low-temperature microwave pyrolysis process with low pollution emissions. The new technology harvests energy from troublesome garbage, reduces waste disposal volume by 55~88%, and produces cleaner, low-toxicity residual, easy-to-store fuel that can be used in general internal combustion engines. Standardized modular equipment provides an effective solution for resilient energy systems, and its easy scalability can reduce the load on the basic grid and improve the stability and dispatchability of energy supply. This research will realize on-site waste treatment, reduce transportation energy consumption, meet regional energy demands, and apply it to coastal, remote villages, offshore platforms, and emergency scenarios.

Different electoral districting affects the election results. The spirit of democracy is worthwhile that the election system must be fair and square and legitimately practices without the occurrence of that the political party and candidate taking advantages on the electoral districting inducing controversy over manipulating the election strategy. In this study, we developed a computational geometry based partitioning algorithm to solve the multi-region electoral districting problems. The algorithm uses the Geographic Information System as well as the dynamic programming techniques and solves the multi-region districting problems by recursively applying the two-partitioning algorithm. The problem regarding to the huge feasible solutions can be reduced substantially by introducing the concept of "indivisible regions". The contiguity test and the compactness test can be done through the knowledge of computational geometry. The method is implemented on a local county to illustrate the entire mechanism and obtained feasible solutions for further evaluation and analysis. We also used the historical voting results to evaluate our districting results and compared with the results released by CEC (Central Election Commission). The analyses show that our methods can solve the multi-region electoral districting problems successfully and effectively.

Traditional electoral districting is mostly carried out by artificial division. It is not only time-consuming and labor-intensive, but it is also difficult to maintain the principles of fairness and consistency. Due to specific political interests, objectivity is usually distorted and controversial in a proxy-election. In order to reflect the spirit of democracy, this study uses computing technologies to automatically divide the constituency and use the concepts of “intelligent clustering” and “extreme arrangement” to conquer many shortcomings of traditional artificial division. In addition, various informational technologies are integrated to obtain the most feasible solutions within the maximum capabilities of the computing system, yet without sacrificing the global representation of the solutions. We take Changhua County, Taiwan as an example of complete electoral districting, and find better results relative to the official version, which obtained a smaller difference in the population of each constituency, more complete and symmetrical constituencies, and fewer regional controversies. Our results demonstrate that multidimensional algorithms using a geographic information system could solve many problems of block districting to make decisions based on different needs.