High Wisdom Intelligence- Discussing Education of High Wisdom Intelligence

The number of talented persons who are equipped with high wisdom intelligence will immediately determine the quality of humankind’s entire life, and the future trend for humankind’s innovation and creation in fields such as thinking, cognition, society, politics, economy, military, science, arts, culture, and so on. Meanwhile, it protects the harmonious development of various fields. By illustrating the meanings of applying high wisdom intelligence in the survival and prosperity for each nation, ethnicity and individual, this article has a significant application to the understanding of educational studies of highwisdom intelligence.

Automated Chinese Essay Scoring using Pre-Trained Language Models

Automated Essay Scoring (AES) aims to assign a proper score to an essay written by a given prompt, which is a significant application of Natural Language Processing (NLP) in the education area. In this work, we focus on solving the Chinese AES problem by Pre-trained Language Models (PLMs) including state-of-the-art PLMs BERT and ERNIE. A Chinese essay dataset has been built up in this work, by which we conduct extensive AES experiments. Our PLMs-based AES models acquire 68.70% in Quadratic Weighted Kappa (QWK), which outperform classic feature-based linear regression AES model. The results show that our methods effectively alleviate the dependence on manual features and improve the portability of AES models. Furthermore, we acquire well-performed AES models with a limited scale of the dataset, which solves the lack of datasets in Chinese AES.

Recent Advances in Dialogue Machine Translation

Recent years have seen a surge of interest in dialogue translation, which is a significant application task for machine translation (MT) technology. However, this has so far not been extensively explored due to its inherent characteristics including data limitation, discourse properties and personality traits. In this article, we give the first comprehensive review of dialogue MT, including well-defined problems (e.g., 4 perspectives), collected resources (e.g., 5 language pairs and 4 sub-domains), representative approaches (e.g., architecture, discourse phenomena and personality) and useful applications (e.g., hotel-booking chat system). After systematical investigation, we also build a state-of-the-art dialogue NMT system by leveraging a breadth of established approaches such as novel architectures, popular pre-training and advanced techniques. Encouragingly, we push the state-of-the-art performance up to 62.7 BLEU points on a commonly-used benchmark by using mBART pre-training. We hope that this survey paper could significantly promote the research in dialogue MT.

Research Advances of d-allulose: An Overview of Physiological Functions, Enzymatic Biotransformation Technologies, and Production Processes

d-allulose has a significant application value as a sugar substitute, not only as a food ingredient and dietary supplement, but also with various physiological functions, such as improving insulin resistance, anti-obesity, and regulating glucolipid metabolism. Over the decades, the physiological functions of d-allulose and the corresponding mechanisms have been studied deeply, and this product has been applied to various foods to enhance food quality and prolong shelf life. In recent years, biotransformation technologies for the production of d-allulose using enzymatic approaches have gained more attention. However, there are few comprehensive reviews on this topic. This review focuses on the recent research advances of d-allulose, including (1) the physiological functions of d-allulose; (2) the major enzyme families used for the biotransformation of d-allulose and their microbial origins; (3) phylogenetic and structural characterization of d-allulose 3-epimerases, and the directed evolution methods for the enzymes; (4) heterologous expression of d-allulose ketose 3-epimerases and biotransformation techniques for d-allulose; and (5) production processes for biotransformation of d-allulose based on the characterized enzymes. Furthermore, the future trends on biosynthesis and applications of d-allulose in food and health industries are discussed and evaluated in this review.

Hybrid Materiality

One of the most recognisable aspects of the digitalization of the design process is the increasing use of 3D printers and robots in the construction phases. When combining with analogue techniques, hybrid materiality in construction and architecture opens up significant application opportunities, but also challenges to the design process and production. In this study, the design and hybrid fabrication of a freeform gridshell structure, placed as a temporary installation in a public park, is presented. The structure was materialized in a hybrid combination of 129 wooden battens and 68 PLA 3D printed nodes, spanning 5 meters and reaching 2.5 m height. The examination of the challenges posed by the hybrid fabrication of the structure suggests that pre-conceived perceptions on production and installation stages should be reconsidered, and it highlights the importance of integrating material constraints and time limitations in the earliest phases of the design process.

Incentives for Plastic Recycling: How to Engage Citizens in Active Collection. Empirical Evidence from Spain

The recycling target for plastics is expected to increase Europe-wide from 22.5% to 55% by 2025, hence the relevance of incentive schemes and the need to reach conclusions about how to encourage families to recycle more. Following this objective, a pilot project was implemented and a virtual reward token called RECICLOS created to encourage recycling among families, using incentives and awards to improve recycling behaviour and a webapp prototype to register the recycled plastic. By the end of the 6-week pilot project, 1053 families were registered on the scheme, representing 10% of the targeted population in the pilot area of the county of Pla de l’Estany, Catalonia, Spain. The novelties were the introduction of a token, the gamification of incentives through raffles and lotteries, webapp-based direct communication with citizens, and feedback after collecting and registering the recycled material. The multidimensional aspects of recycling activities, their strong relation with human behavioural patterns, and the high demand for communication and interaction mean that mobile technologies find significant application in this field. The results show that people can be influenced and their recycling habits changed by means of varied, effective, and innovative incentive schemes.

Machine Learning in Estimating CO2 Emissions from Electricity Generation

In the last decades, there has been an outstanding rise in the advancement and application of various types of Machine learning (ML) approaches and techniques in the modeling, design and prediction for energy systems. This work presents a simple but significant application of a ML approach, the Support Vector Machine (SVM) to the estimation of CO2 emission from electricity generation. The CO2 emission was estimate in a framework of Cost-Effectiveness Analysis between two competing technologies in electricity generation using data for Combined Cycle Gas Turbine Plant (CCGT) provided by IEA for Italy in 2020. Respect to other application of ML techniques, usually developed to address engineering issues in energy generation, this work is intended to provide useful insights in support decision for energy policy.

Radio Mean Labeling Of Paths And Its Total Graph

A graph labeling problem is an assignment of labels to the vertices or edges (or both) of a graph G satisfying some mathematical condition. Radio Mean Labeling, a vertex-labeling of graphs with non-negative integers has a significant application in the study of problems related to radio channel assignment. The maximum label used in a radio mean labeling is called its span, and the lowest possible span of a radio mean labeling is called the radio mean number of a graph. In this paper, we obtain the radio mean number of paths and total graph of paths.

The Implications of Ferroelectric FET Device Models to the Design of Computing-in-Memory Architectures

Data transfer between a processor and memory frequently represents a bottleneck with respect to improving application-level performance. Computing-in-memory (CiM), where logic and arithmetic operations are performed in memory, could significantly reduce both energy consumption and computational overheads associated with data transfer. This work presents a revisited study of FeFET-CiM, a CiM architecture capable of performing Boolean ((N)AND, (N)OR, X(N)OR, INV) as well as arithmetic (ADD) operations between words in memory. In this study, we employ two types of FeFET-based memory cells in the CiM architecture. Namely, the 2T+1FeFET and the 1-FeFET memory cells. The use of these two types of memory cells in the FeFET-CiM architecture is enabled by two distinct models for FeFET devices. The FeFET-CiM architecture based on 2T+1FeFETs (1FeFETs) offers an average speedup of ∼2.5X (∼1.1X) and energy reduction of ∼1.7X (∼1.4X) when compared to a SRAM baseline across 12 benchmark programs. Despite smaller speedups and energy savings enabled by 1FeFET-CiM when compared to 2T+1FeFET-CiM, 1FeFET memory arrays may offer up to ∼5.3X density improvements when compared to conventional 6T-SRAM arrays. Furthermore, 1FeFET-CiM offers significant application-level improvements when compared to a counterpart STT-CiM architecture.