Empirical Research of “Java Programming” Practical Training Base on OBE Theory

This paper demonstrates the eff ectiveness of outcome based (OBE) teaching of practical training course in object-oriented programming in Java by designing a set of OBE project-oriented teaching model applicable to Java practical training of object-oriented programming in higher vocational colleges. The fi ndings show that outcome based (OBE) teaching is eff ective in the practical training of object-oriented programming in Java and serves as an experimental research example for the higher vocational programming practical training course

Perception Analysis: Pro- and Anti- Vaccine Classification with NLP and Machine Learning

Online discussion of the ensuing pandemic exemplifies the extent and complexity of information required to understand human perception. Social media has proven to be a viable medium for identifying actionable data and analyzing public perception. As health sectors all over the world battled to obtain accurate information regarding COVID-19, this research focused on gauging public perceptions of the vaccine. The public reception of the vaccine can be determined by public perception. This study explores how to use machine learning to understand human perceptions in the context of the COVID-19 vaccine. Natural Language Processing (NLP) was employed to detect pro- and anti-vaccine tweets, while two machine learning classification models were used to study the patterns derived from the analysis. The study analyzed people's perceptions of the vaccine by presenting the results from a geographic region, while learning patterns that are likely to be associated with pro- or anti-vaccine perceptions

Oxymatrine induces human pancreatic cancer PANC-1 cells apoptosis via regulating expression of Bcl-2 and IAP families, and releasing of cytochrome c

<p>Abstract</p> <p>Background</p> <p>Oxymatrine, an isolated extract from traditional Chinese herb <it>Sophora Flavescens Ait</it>, has been traditionally used for therapy of anti-hepatitis B virus, anti-inflammation and anti-anaphylaxis. The present study was to investigate the anti-cancer effect of oxymatrine on human pancreatic cancer PANC-1 cells, and its possible molecular mechanism.</p> <p>Methods</p> <p>The effect of oxymatrine on the viability and apoptosis was examined by methyl thiazolyl tetrazolium and flow cytometry analysis. The expression of Bax, Bcl-2, Bcl-x (L/S), Bid, Bad, HIAP-1, HIAP-2, XIAP, NAIP, Livin and Survivin genes was accessed by RT-PCR. The levels of cytochrome c and caspase 3 protein were assessed by Western blotting.</p> <p>Results</p> <p>Oxymatrine inhibited cell viability and induced apoptosis of PANC-1 cells in a time- and dose-dependent manner. This was accompanied by down-regulated expression of Livin and Survivin genes while the Bax/Bcl-2 ratio was upregulated. Furthermore, oxymatrine treatment led to the release of cytochrome c and activation of caspase-3 proteins.</p> <p>Conclusion</p> <p>Oxymatrine can induce apoptotic cell death of human pancreatic cancer, which might be attributed to the regulation of Bcl-2 and IAP families, release of mitochondrial cytochrome c and activation of caspase-3.</p

Computer-Aided Diagnosis of Alzheimer’s Disease via Deep Learning Models and Radiomics Method

This paper focused on the problem of diagnosis of Alzheimer’s disease via the combination of deep learning and radiomics methods. We proposed a classification model for Alzheimer’s disease diagnosis based on improved convolution neural network models and image fusion method and compared it with existing network models. We collected 182 patients in the ADNI and PPMI database to classify Alzheimer’s disease, and reached 0.906 AUC in training with single modality images, and 0.941 AUC in training with fusion images. This proved the proposed method has better performance in the fusion images. The research may promote the application of multimodal images in the diagnosis of Alzheimer’s disease. Fusion images dataset based on multi-modality images has higher diagnosis accuracy than single modality images dataset. Deep learning methods and radiomics significantly improve the diagnosing accuracy of Alzheimer’s disease diagnosis

Daily reference evapotranspiration prediction for irrigation scheduling decisions based on the hybrid PSO-LSTM model.

The shortage of available water resources and climate change are major factors affecting agricultural irrigation. In order to improve the irrigation water use efficiency, it is necessary to predict the water requirements for crops in advance. Reference evapotranspiration (ETo) is a hypothetical standard reference crop evapotranspiration, many types of artificial intelligence models have been applied to predict ETo; However, there are still few in the literature regarding the application of hybrid models for deep learning model parameters optimization. This paper proposes two hybrid models based on particle swarm optimization (PSO) and long-short-term memory (LSTM) neural network, used to predict ETo at the four climate stations, Shaanxi province, China. These two hybrid models were trained using 40 years of historical data, and the PSO was used to optimize the hyperparameters in the LSTM network. We applied the optimized model to predict the daily ETo in 2019 under different datasets, the result showed that the optimized model has good prediction accuracy. The optimized hybrid models can help farmers and irrigation planners to make plan earlier and precisely, and can provide valuable information to improve tasks such as irrigation planning

Thermal Properties and the Prospects of Thermal Energy Storage of Mg–25%Cu–15%Zn Eutectic Alloy as Phase Change Material

This study focuses on the characterization of eutectic alloy, Mg–25%Cu–15%Zn with a phase change temperature of 452.6 °C, as a phase change material (PCM) for thermal energy storage (TES). The phase composition, microstructure, phase change temperature and enthalpy of the alloy were investigated after 100, 200, 400 and 500 thermal cycles. The results indicate that no considerable phase transformation and structural change occurred, and only a small decrease in phase transition temperature and enthalpy appeared in the alloy after 500 thermal cycles, which implied that the Mg–25%Cu–15%Zn eutectic alloy had thermal reliability with respect to repeated thermal cycling, which can provide a theoretical basis for industrial application. Thermal expansion and thermal conductivity of the alloy between room temperature and melting temperature were also determined. The thermophysical properties demonstrated that the Mg–25%Cu–15%Zn eutectic alloy can be considered a potential PCM for TES

Printable Stretchable Silver Ink and Application to Printed RFID Tags for Wearable Electronics

A printable elastic silver ink has been developed, which was made of silver flakes, dispersant, and a fluorine rubber and could be sintered at a low temperature. The printed elastic conductors showed low resistivity at 21 &mu;&Omega;&middot;cm, which is about 13.2 times of bulk silver (1.59 &mu;&Omega;&middot;cm). Their mechanical properties were investigated by bending, stretching, and cyclic endurance tests. It was found that upon stretching the resistance of printed conductors increased due to deformation and small cracks appeared in the conductor, but was almost reversible when the strain was removed, and the recovery of conductivity was found to be time dependent. Radio-frequency identification (RFID) tags were fabricated by screen printing the stretchable silver ink on a stretchable fabric (lycra). High performance of tag was maintained even with 1000 cycles of stretching. As a practical example of wearable electronics, an RFID tag was printed directly onto a T-shirt, which demonstrated its normal working order in a wearing state

Fabrication, Structure, and Thermal Properties of Mg–Cu Alloys as High Temperature PCM for Thermal Energy Storage

This work studied the thermophysical properties of Mg-24%Cu, Mg-31%Cu, and Mg-45%Cu (wt.%) alloys to comprehensively consider the possibility of using them as thermal energy storage (TES) phase change materials (PCMs) used at high temperatures. The microstructure, phase composition, phase change temperatures, and enthalpy of these alloys were investigated by an electron probe micro analyzer (EPMA), X-ray diffraction (XRD), and differential scanning calorimetry (DSC). The XRD and EPMA results indicated that the binary eutectic phase composed of α-Mg and Mg2Cu exists in the microstructure of the prepared Mg–Cu series alloys. The microstructure of Mg-24%Cu and Mg-31%Cu is composed of α-Mg matrix and binary eutectic phases, and Mg-45%Cu is composed of primary Mg2Cu and binary eutectic phases. The number of eutectic phases is largest in Mg-31%Cu alloy. The DSC curves indicated that the onset melting temperature of Mg-24%Cu, Mg-31%Cu, and Mg-45%Cu alloys were 485, 486, and 485 °C, and the melting enthalpies were 152, 215, and 91 J/g. Thermal expansion and thermal conductivity were also determined, revealing that the Mg–Cu alloys had a low linear thermal expansion coefficient and high thermal conductivity with respect to increasing temperatures. In conclusion, the thermal properties demonstrated that the Mg–Cu alloys can be considered as a potential PCM for TES