Development and Initial Validation of a Flipped Classroom Adoption Inventory in Higher Education

The purpose of this study is to develop and initially validate an inventory to learn about the critical variables involved in a higher education instructor’s decision to adopt a flipped classroom instructional model. A flipped classroom is an instructional model in which students’ learning is divided into two phases, the self-directed pre-class learning phase and the in-class student-centered active learning phase. Both phases are typically technology-enhanced. This study addresses a gap in the recent research regarding the identification and assessment of the critical variables that are related to a higher education instructor’s decision to adopt a flipped classroom instructional model. This study proposed a six-scale, 43-item inventory on higher education instructors’ adoption decision of a flipped classroom instructional model. After pilot study, this inventory was released to instructors at UTK through a web-based survey software tool and received more than 200 valid responses. A validated and refined inventory was generated after an Exploratory Factor Analysis (EFA), which was used to identify the factor structure and the relationship between items and the factors. This validated inventory includes 24 items in three subscales, which represent three factors that might influence a higher education instructor’s adoption decision of a flipped classroom instructional model. Then, the three factors were used as independent variables in a multiple regression to examine their ability to predict a higher education instructor’s adoption decision. The results revealed that performance expectancy and technology self- efficacy are strong predictors of a higher education instructor’s decision to adopt a flipped classroom instructional model

Effect of Dietary Supplementation with Mannose Oligosaccharides on the Body Condition, Lactation Performance and Their Offspring of Heat-Stressed Sows

The aim of this study was to determine the effects of dietary supplementation with mannose oligosaccharide (MOS) on the condition of the body and the reproductive and lactation performances of sows. Eighty pregnant sows were randomly assigned to four groups with a 2 × 2 factorial design: with or without MOS (1 g/kg) and with or without heat stress (HS) challenge. The temperature in the HS groups (HS and HM group) was controlled at 31.56 ± 1.22 °C, while the temperature in the active cooling (AC) groups (AC and AM group) was controlled at 23.49 ± 0.72 °C. The weight loss of sows in the AC group was significantly lower than that of sows in the HS group (p < 0.01). The weight and backfat thickness loss of sows supplemented with MOS displayed a downward trend. The average birth weight of the litter significantly increased in the HM group (basic diet + MOS) compared with the HS group (p < 0.05). The milk protein of sows significantly decreased under the HS condition at 2 and 12 h after delivery (p < 0.05). However, the milk immunoglobin G (IgG) of sows in the HS group increased significantly compared with that of sows in the HM group (p < 0.05) at 12 and 24 h after delivery. The levels of serum urea nitrogen (UREA) and glucose (GLU) decreased significantly under the HS condition (p < 0.05), while the level of interleukin-6 (IL-6) increased significantly under the HS condition (p < 0.05). Dietary supplementation with MOS also significantly reduced TNF-α under the AC conditions (p < 0.05). In conclusion, HS significantly affected the body condition, lactation performances and their offspring of sows. However, dietary supplementation with 1 g/kg MOS did not result in statistically significant changes

The Importance of Expert Knowledge for Automatic Modulation Open Set Recognition

Automatic modulation classification (AMC) is an important technology for the monitoring, management, and control of communication systems. In recent years, machine learning approaches are becoming popular to improve the effectiveness of AMC for radio signals. However, the automatic modulation open-set recognition (AMOSR) scheme that aims to identify the known modulation types and recognize the unknown modulation signals is not well studied. Therefore, in this paper, we propose a novel multi-modal marginal prototype framework for radio frequency (RF) signals ( MMPRF ) to improve AMOSR performance. First, MMPRF addresses the problem of simultaneous recognition of closed and open sets by partitioning the feature space in the way of one versus other and marginal restrictions. Second, we exploit the wireless signal domain knowledge to extract a series of signal-related features to enhance the AMOSR capability. In addition, we propose a GAN-based unknown sample generation strategy to allow the model to understand the unknown world. Finally, we conduct extensive experiments on several publicly available radio modulation data, and experimental results show that our proposed MMPRF outperforms the state-of-the-art AMOSR methods

Highly efficient mass production of boron nitride nanosheets via a borate nitridation method

Boron nitride nanosheets (BNNSs) have attracted intensive attention because of their fantastic properties, including excellent electrical insulating ability, splendid thermal conductivity, and outstanding oxidation resistance. However, facing the rising demand for versatile applications, the cost-effective mass production of BNNSs, similar to graphene, remains a huge challenge. Here, we provide a highly effective strategy for BNNS synthesis via a borate nitridation method utilizing solid borate precursors, producing gram-scale yields with efficiencies up to 88%. Combined with density functional theory (DFT) calculations, a vapor–solid–solid (VSS) mechanism was proposed in which ammonia vapor reacts with the solid borates, producing solid BNNSs at the vapor–solid interfaces. The strategy proposed herein, together with the diversity of borate compounds, allows numerous choices for the facile mass production of BNNSs at low cost. In addition, the remarkably enhanced thermal conductivity in composite materials demonstrated good quality and huge potential for these BNNSs in thermal management. This work reveals a cost-efficient method for the large-scale production of BNNSs, which should promote practical applications in various fields