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The aim of this paper is to solve the problem of lack of real context in JFL (Japanese as Foreign Language) classroom with video corpus-based teaching. It also offers reference for the development of video corpus.

The authors designed an intelligent Japanese online video corpus, namely the JV Finder, which is a corpus of Japanese films and TV series. The authors applied the JV Finder to JFL teaching to solve the problem of lack of real context and designed several teaching experiments to validate its benefits.

The results of teaching experiments show that the video corpus-based teaching significantly improves the learning effect. The JV Finder can help students memorize vocabularies and understand the meaning of new vocabularies in a better way.

There are still some differences in language context between real life and films, which cannot fully reflect the state of native speaker in real life. Meanwhile, the number of students participating in this experiment is relatively small, so the universality of the result need further study.

This study combined linguistics with software engineering to solve the problem of lack of real context. Video corpus-based teaching not only can be used in Japanese teaching field but also provide value for other foreign language teaching.

The JV Finder has obtained Chinese national patent license (patent no. 20131118). The video corpus (the JV Finder) has a far-reaching impact on JFL teaching.

This paper provides an intelligent Japanese online video corpus. It is applied to JFL teaching to solve the problem of lack of real context. The findings show that the video corpus can significantly improve the effectiveness of Japanese learning.

The purpose of this paper is to study the microstructure and properties of Sn-3.5Ag and Sn-3.5Ag-0.5Sb lead-free solder alloys with and without a rotating magnetic field (RMF).

Optical microscopy, scanning electron microscopy and X-ray diffraction were used to analyze the effect of an RMF on the microstructure of the solders. Differential scanning calorimetry was used to study the influence of the RMF on the thermal characteristics of the solders. The mechanical properties of the alloys were determined by tensile measurements at different strain rates.

The ß-Sn grains and intermetallic compounds for the Sn-3.5Ag and Sn-3.5Ag-0.5Sb lead-free solder alloys were refined under an RMF, and the morphology of the ß-Sn grains changed from dendritic to equiaxed. The pasty range was significantly reduced under an RMF. The ultimate tensile strength (UTS) of Sn-3.5Ag improved under the RMF, whereas the UTS of Sn-3.5Ag-0.5Sb decreased slightly. The addition of Sb to the Sn-3.5Ag alloy significantly enhanced the UTS and elongation (El.%) of the samples. The UTS of the solder increased with increasing strain rate.

The results revealed that the application of RMF in the molten alloy had a significant effect on its microstructure and mechanical properties. The thermal characteristics of the Sn-3.5Ag and Sn-3.5Ag-0.5Sb solder alloys were improved under the RMF. This research is expected to fill a knowledge gap regarding the behaviour of Sn-Ag solder alloys under RMF.

Project portfolio benefit (PPB) evaluation is crucial for project portfolio management decisions. However, PPB is complex in composition and affected by synergy and ambidexterity. Ignoring these characteristics can result in inaccurate assessments, impeding the management and optimization of benefit. Considering the above complexity of PPB evaluation, this study aims to propose a refined PPB evaluation model to provide decision support for organizations.

A back propagation neural network optimized via genetic algorithm and pruning algorithm (P-GA-BPNN) is constructed for PPB evaluation. First, the benefit evaluation criteria are established. Second, the inputs and expected outputs for model training and testing are determined. Then, based on the optimization of BPNN via genetic algorithm and pruning algorithm, a PPB evaluation model is constructed considering the impacts of ambidexterity and synergy on PPB. Finally, a numerical example was applied to validate the model.

The results indicate that the proposed model can be used for effective PPB evaluation. Moreover, it shows superiority in terms of MSE and fitting effect through extensive comparative experiments with BPNN, GA-BPNN, and SVM models. The robustness of the model is also demonstrated via data random disturbance experiment and 10-cross-validation. Therefore, the proposed model could serve as a valuable decision-making tool for PPB management.

This study extends prior research by integrating the impacts of synergy and ambidexterity on PPB when conducting PPB evaluation, which facilitates to manage and enhance PPB. Besides, the structural redundancy of existing assessment methods is solved through the dynamic optimization of the network structure via the pruning algorithm, enhancing the effectiveness of PPB decision-making tools.

The purpose of this study is to investigate the effects of Ce and Sb doping on the microstructure and thermal mechanical properties of Sn-1.0Ag-0.5Cu lead-free solder. The effects of 0.5%Sb and 0.07%Ce doping on microstructure, thermal properties and mechanical properties of Sn-1.0Ag-0.5Cu lead-free solder were investigated.

According to the mass ratio, the solder alloys were prepared from tin ingot, antimony ingot, silver ingot and copper ingot with purity of 99.99% at 400°C. X-ray diffractometer was adopted for phase analysis of the alloys. Optical microscopy, scanning electron microscopy and energy dispersive spectrometer were used to study the effect of the Sb and Ce doping on the microstructure of the solder. Then, the thermal characteristics of alloys were characterized by a differential scanning calorimeter (DSC). Finally, the ultimate tensile strength (UTS), elongation (EL.%) and yield strength (YS) of solder alloys were measured by tensile testing machine.

With the addition of Sb and Ce, the ß-Sn and intermetallic compounds of solders were refined and distributed more evenly. With the addition of Sb, the UTS, EL.% and YS of Sn-1.0Ag-0.5Cu increased by 15.3%, 46.8% and 16.5%, respectively. The EL.% of Sn-1.0Ag-0.5Cu increased by 56.5% due to Ce doping. When both Sb and Ce elements are added, the EL.% of Sn-1.0Ag-0.5Cu increased by 93.3%.

The addition of 0.5% Sb and 0.07% Ce can obtain better comprehensive performance, which provides a helpful reference for the development of Sn-Ag-Cu lead-free solder.

The purpose of this research is to propose a flexible sensor with a weft-knitted float stitch structure and to explore knitting techniques that allow conductive yarns to be skin-tight and less exposed, reducing production processes and increasing productivity. Study its electrical conductivity in different yarn materials, knit processes and deformation ranges. The analysis is compared to provide some basis for the design of the electrodes.

The method includes five operations: (1) Analysis of the morphological appearance, tensile variation, fiber material properties and electrical conductivity of high-elastic and filament silver-plated conductive yarns. (2) Based on the knitting process of the floating yarn structure, three-dimensional modeling of the flexible sensor was carried out to explore the influence of knitting process changes on appearance characteristics. (3) The fabric samples are knitted by different silver-plated conductive yarns with different structures. Processing of experimental samples to finished size by advance shrinkage. (4) Measure the resistance of the experimental sample after the machine has been lowered and after pre-shrinking. Use the stretching machine to simulate a wearing experiment and measure the change in resistance of the sample in the 0–15% stretching range. (5) Analyze the influence factors on the conductive performance of the flexible sensor to determine whether it is suitable for textile flexible sensors.

For the float knitted flexible sensors, the floating wire projection is influenced by the elasticity of the fabric and the length of the floating wire. Compared to the plain knitted flexible sensors, it has less resistance variation and better electrical properties, making it suitable for making electrodes for textile structures. In addition, the knitting method is integrated with the intelligent monitoring clothing, which saves the process for the integration of the flexible sensor, realizes positioning and fixed-point knitting.

The sensor technology of the designed weft-knitted float structure is varied and can be freely combined and designed in a wide range. Within the good electrical conductivity, the flexible sensor can realize integrated knitting, positioning monitoring, integrating into the appearance of clothing. It can also focus on the wearing experience of wearable products so that the appearance of the monitoring clothing is close to the clothes we wear in our daily life.

In this paper, an integrated positioning knitting flexible sensor based on the weft knitting float structure is studied. The improved knitting process allows the sensing contact surface to be close to the skin and reduces the integration process. The relationship between the exposure of the silver-plated yarn on the clothing surface and the electrical conductivity is analyzed. Within a certain conductive performance, reduces the exposed area of the conductive yarn on the clothing surface and proposes a design reference for the flexible sensor appearance.

This study aims to take boutique luxury hotels as its research subject to enrich the view on the self-positioned image of luxury hotels and simultaneously compare this image with the one perceived by customers. It also investigates whether a gap exists between the two images.

The best-rated boutique luxury hotel in Hong Kong is selected as the case hotel. Through the interpretation and discussion of high-frequency wordlists and semantic co-occurrence network charts, major topics in the hotel’s self-presented image and customers’ perceived image are identified accordingly.

Five dimensions (event making, exquisite food, excellent amenities and services, special function venue and promotion) used by hotels to form their boutique luxury image are recognised. Between hotels’ self-positioned image and customers’ perceived images, minor similarities such as the recognition of excellent amenities and services were confirmed but considerable differences were identified, indicating the ineffective marketing by the boutique luxury hotel despite its success in terms of ranking. Managerial suggestions on how to address the image discrepancy were proposed.

This study contributes insights into hotel image discrepancy among social media platforms in luxury hotel segments by adding the category of boutique luxury hotels. It also identifies the implications for enhancing the development of a hotel image to meet customers’ needs and expectations.

酒店的自我定位形象与顾客感知形象——以香港某精品豪华酒店为例

本研究试图以精品豪华酒店为研究对象, 丰富豪华酒店自我定位形象的视角, 同时将其与顾客感知的形象进行比较, 考察二者之间是否存在差距。

本文选取香港评分最佳的精品豪华酒店为案例酒店。通过对高频词表和语义共现网络图的解读和讨论, 识别出酒店自我呈现形象和顾客感知形象中的主要话题。

酒店利用五个维度（活动打造、精致的食物、优质的设施和服务、特殊的功能场所和促销）来塑造其精品豪华形象。在酒店的自我定位形象和顾客的感知形象之间, 发现了一些细微的相似之处, 如对优质设施和服务的认可, 但差异之处相当大, 表明尽管精品豪华酒店取得了优秀的排名, 但其营销效果不佳。本文提出了解决形象差异的管理建议。

本研究通过增加精品豪华酒店类别的案例, 有助于深入了解豪华酒店细分市场中社交媒体平台之间的酒店形象差异。本文还确定了促进酒店形象发展的影响, 以满足客户需求和期望。

Imagen auto-posicionada del hotel versus imagen percibida por los clientes: un estudio de caso de un hotel boutique de lujo en Hong Kong.

Este estudio se esfuerza por tomar a los hoteles boutique de lujo como tema de investigación para profundizar en el conocimiento sobre la imagen de los hoteles de lujo y compar el posicionamiento presentado por el hotel con el que perciben los clientes. También investiga si existe una brecha entre las dos imágenes.

Para el caso se selecciona el mejor hotel de lujo calificado en Hong Kong. A través de la interpretación y discusión de listas de palabras de alta frecuencia y gráficos de redes de coincidencia semántica se identifican los principales temas en la imagen presentada por el hotel y la imagen percibida de los clientes.

Se reconocen cinco dimensiones (creación de eventos, comida exquisita, excelentes comodidades y servicios, lugar de funciones especiales y promoción) utilizadas por los hoteles para formar su imagen de lujo boutique. Entre la imagen auto-posicionada de los hoteles y las imágenes percibidas por los clientes, se confirmaron pequeñas similitudes, como el reconocimiento de excelentes comodidades y servicios, pero se identificaron diferencias considerables, lo que indica la comercialización ineficaz del hotel boutique de lujo a pesar de su éxito en términos de clasificación. Se propusieron sugerencias gerenciales sobre cómo abordar la discrepancia de imagen.

Este estudio aporta información sobre la discrepancia de imagen del hotel entre las plataformas de redes sociales en los segmentos de hoteles de lujo al agregar la categoría de hoteles boutique de lujo. También identifica las implicaciones para mejorar el desarrollo de una imagen de hotel que satisfaga las necesidades y expectativas del cliente.

The purposes are to explore corporate financial management optimization in the context of big data and provide a sustainable financial strategy for corporate development.

First, the shortcomings of the traditional financial management model are analyzed under the background of big data analysis. The big data analytic technology is employed to extract financial big data information and establish an efficient corporate financial management model. Second, the deep learning (DL) algorithm is applied to implement a corporate financial early-warning model to predict the potential risks in corporate finance, considering the predictability of corporate financial risks. Finally, a corporate value-centered development strategy based on sustainable growth is proposed for long-term development.

The experimental results demonstrate that the financial early-warning model based on DL has an accuracy of 90.7 and 88.9% for the two-year financial alert, which is far superior to the prediction effect of the traditional financial risk prediction models.

The obtained results can provide a reference for establishing a sustainable development pattern of corporate financial management under the background of big data.

The purpose of this paper is to empirically analyze the effects of stock price synchronicity and herding behavior of qualified foreign institutional investors (QFII) on stock price crash risk, especially the mediating effect of herding behavior of QFII on the relation of stock price synchronicity and stock price crash risk.

Taking China’s A-share listed companies from 2005 to 2014 and QFII holding shares data as the research sample, this study calculates herding effect index, sock price synchronicity index and stock price crash risk index, and perform linear regression.

This study concludes that, either herding behavior of QFII or the stock price synchronicity can increase the stock price crash risk. Further study reveals that, the herding behavior of QFII also improves the effect of stock price synchronicity on stock price crash risk. Namely, herding behavior of QFII acts as the mediating role between stock price synchronicity and stock price crash risk.

This study empirically analyzes and verifies the mediating roles of herding behavior of QFII in affecting the relation of sock price synchronicity and stock price crash risk for the first time. The findings of this study contribute to the study of the role of QFII in stabilizing Chinese security market.

The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations (NSS). Because the conventional methods for the prediction of NSS, such as support vector machine, particle swarm optimization, etc., lack accuracy, robustness and efficiency, in this study, the authors propose a new method for the prediction of NSS based on recurrent neural network (RNN) with gated recurrent unit.

This method extracts internal and external information features from the original time-series network data for the first time. Then, the extracted features are applied to the deep RNN model for training and validation. After iteration and optimization, the accuracy of predictions of NSS will be obtained by the well-trained model, and the model is robust for the unstable network data.

Experiments on bench marked data set show that the proposed method obtains more accurate and robust prediction results than conventional models. Although the deep RNN models need more time consumption for training, they guarantee the accuracy and robustness of prediction in return for validation.

In the prediction of NSS time-series data, the proposed internal and external information features are well described the original data, and the employment of deep RNN model will outperform the state-of-the-arts models.

In the past few years, millions of people started to acquire knowledge from the Massive Open Online Courses (MOOCs). MOOCs contain massive video courses produced by instructors, and learners all over the world can get access to these courses via the internet. However, faced with massive courses, learners often waste much time finding courses they like. This paper aims to explore the problem that how to make accurate personalized recommendations for MOOC users.

This paper proposes a multi-attribute weight algorithm based on collaborative filtering (CF) to select a recommendation set of courses for target MOOC users.

The recall of the proposed algorithm in this paper is higher than both the traditional CF and a CF-based algorithm – uncertain neighbors’ collaborative filtering recommendation algorithm. The higher the recall is, the more accurate the recommendation result is.

This paper reflects the target users’ preferences for the first time by calculating separately the weight of the attributes and the weight of attribute values of the courses.