An Evaluation of Core Competence on Knowledge Management for Elementary Schools’ Teachers: A Case Study of Remote Rural Area in Taiwan

In the knowledge economy era, teachers’ core competencies for knowledge management have an important impact on the development of school education. It is an important issue to explore how to evaluate core competencies of knowledge management for elementary schools’ teachers. This requires a thorough investigation to gain core competencies. Hence, the main purpose of this paper is to present a Matrix Analysis (MA) approach for evaluating core competencies of knowledge management and to describe the applications of the MA approach for elementary schools’ teachers. An empirical analysis is performed to demonstrate the computational process of the MA approach adopted by this paper. Study results show that eleven core competencies are identified. They are ‘finding problems,’ ‘flexibility,’ ‘interpersonal communication,’ ‘oral communication skills,’ ‘knowledge selection,’ ‘activities recording,’ ‘knowledge application on work planning,’ ‘research data application,’ ‘transforming knowledge into concrete actions,’ ‘interpreting results,’ and ‘judging knowledge value,’ respectively. It is suggested that more attentions should be paid to exploit these core competencies effectively and then develop the solutions, which should continuously strengthen the perspective of teachers in order to obtain the competitive advantages in the future

Methyl 4-[(5-chloro­pyrimidin-2-yl)carbamo­yl]benzoate

Mol­ecules of the title compound, C13H10ClN3O3, form centrosymmetric dimers via inter­molecular N—H⋯N hydrogen bonds generating an R 2 2(8) motif. The dimers are further connected through an O⋯Cl—C halogen bond [O⋯Cl = 3.233 (1) Å and O⋯Cl—C = 167.33 (1)°] into a chain along [110]. The secondary amide group adopts a cis conformation. Weak C—H⋯N hydrogen bonds among the methyl benzoate and pyrimidyl rings are also observed in the crystal structure

SASMU: boost the performance of generalized recognition model using synthetic face dataset

Nowadays, deploying a robust face recognition product becomes easy with the development of face recognition techniques for decades. Not only profile image verification but also the state-of-the-art method can handle the in-the-wild image almost perfectly. However, the concern of privacy issues raise rapidly since mainstream research results are powered by tons of web-crawled data, which faces the privacy invasion issue. The community tries to escape this predicament completely by training the face recognition model with synthetic data but faces severe domain gap issues, which still need to access real images and identity labels to fine-tune the model. In this paper, we propose SASMU, a simple, novel, and effective method for face recognition using a synthetic dataset. Our proposed method consists of spatial data augmentation (SA) and spectrum mixup (SMU). We first analyze the existing synthetic datasets for developing a face recognition system. Then, we reveal that heavy data augmentation is helpful for boosting performance when using synthetic data. By analyzing the previous frequency mixup studies, we proposed a novel method for domain generalization. Extensive experimental results have demonstrated the effectiveness of SASMU, achieving state-of-the-art performance on several common benchmarks, such as LFW, AgeDB-30, CA-LFW, CFP-FP, and CP-LFW.Comment: under revie

PreFallKD: Pre-Impact Fall Detection via CNN-ViT Knowledge Distillation

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed pre-impact fall detection systems using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms)

N 1,N 2-Bis(6-methyl-2-pyrid­yl)formamidine

In the crystal structure of the title mol­ecule, C13H14N4, the two pyridyl rings are not coplanar but twisted about the C—N bond with an inter­planar angle of 71.1 (1)°. In the crystal, the mol­ecules form dimers, situated on crystallographic centres of inversion, which are connected via a pair of N—H⋯N hydrogen bonds. C—H⋯π-electron ring inter­actions are also present in the crystal structure. The title mol­ecule adopts an s–cis–anti–s–cis conformation in the solid state

Poly[(μ6-benzene-1,3,5-tricarboxyl­ato-κ6 O 1:O 1′:O 3:O 3′:O 5:O 5′)tris­(N,N-dimethyl­formamide-κO)tris­(μ3-formato-κ2 O:O′)trimagnesium(II)]

The title complex, [Mg3(CHO2)3(C9H3O6)(C3H7NO)3]n, exhib­its a two-dimensional structure parallel to (001), which is built up from the MgII atoms and bridging carboxyl­ate ligands (3 symmetry). The MgII atom is six-coordinated by one O atom from a dimethyl­formamide mol­ecule, two O atoms from two μ6-benzene-1,3,5-tricarboxyl­ate ligands and three O atoms from three μ3-formate ligands in a distorted octa­hedral geometry

Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a "long-lie". Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LSTM) for FD