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Crowdsensing exploits the sensing abilities offered by smart phones and users' mobility. Users can mutually help each other as a community with the aid of crowdsensing. The potential of crowdsensing has yet to be fully realized for improving public health. A protocol based on gamification to encoura...

The purpose of this paper examines the potential role of Chinese outbound tourism as a catalyst of change for the Macanese identity. There is an emerging trend to have a new identity amongst Macanese residents to valorize cultural and heritage assets. By using Bourdieu’s concept of habitus, it proposes that the social media space is built to voice concerns of language, place attachment and induced public participation. The study utilizes a mixed method approach comprising a survey for netizens and content analysis of online discussions, in order to fully understand changes due to Chinese outbound tourism.

Limited special education and related services are available for children with autism spectrum disorder (ASD) in Macau, especially those who are educated in general education classrooms. No intervention study has been conducted on these children. This study was conducted to explore the relationship between a board game play intervention and board game play behaviors and social communication of children with ASD educated in general education classrooms in Macau. A repeated measures design was used and the results of this study showed the mean occurrence of unprompted board game play behaviors per session during intervention was not significantly different from that during pre- or post-intervention. The mean occurrence of social communication per session during intervention was significantly higher than that during pre- and post-intervention. These findings suggest a positive relationship existed between the board game intervention used in this study and social communication of children with ASD.

The Mesozoic gold deposits in the North China Craton (NCC) were hosted by the Precambrian basement and Mesozoic intrusions. Thus, most researchers consider that these gold deposits were genetically linked to the Mesozoic intrusions. However, we suggest that a metamorphic devolatilization model provides an alternative based on a combined Fe and in-situ S isotopes study on auriferous pyrites from the Baiyun gold deposit in the NCC. The Triassic Baiyun gold deposit contains the quartz vein and altered rock ores that were developed in the Paleoproterozoic metavolcanic-sedimentary rocks (the Liaohe Group). Our in-situ S isotopic analyses show that pyrites from the quartz vein ores are characterized by negative δ34S values (-10.7 ∼ -5.5‰), while those from the altered rock ores have two distinct groups of δ34S values, one being positive (+13.5 ∼ +16.2‰) and the other negative (-10.6 ∼ -3.0‰). We suggest that pyrite grains with positive δ34S values should be relicts from the host rocks, because they show comparable δ34S values with those from the host rocks schists (+3.3 ∼ +16.1‰). Thus, only the negative δ34S values of pyrites in ores (-10.7 ∼ -3.0‰) and the Fe isotopes of the quartz vein ores (δ56Fe = +0.30 ∼ +0.48‰) can represent the isotopic characteristics of ore-forming fluids at Baiyun. Our study shows that the sulfur were probably from the pyritic volcanic-sedimentary sequences of the Liaohe Group, rather than from magmas. The calculated δ56Fe values of the ore-forming fluids (-0.78 ∼ -0.37‰; pyrite-fluid isotope fractionation) could be modelled in a metamorphic devolatilization model with Fe-species (pyrite&magnetite) of the Liaohe Group as sources. Therefore, our combined S- and Fe- isotope data indicate that the metamorphic devolatilization of the Liaohe Group could account for the genesis of the Baiyun gold deposit.

Abstract As the population of Chinese immigrants has been growing rapidly in the United States, it has been understudied on the parenting behaviours as well as the roles parental stress and social support playing in parenting in this group. This study investigated whether parental stress was associated with parenting and whether this relationship was mediated by social support in a sample of 255 Chinese immigrant parents from the Survey of Asian American Families in New York City. Regression analyses with a rich array of control variables found that a higher level of parental stress and the presence of one or more stressors such as unemployment, low income, and low education were positively associated with the use of harsh discipline and parent?child conflicts and negatively associated with positive parenting practices. Social support functioned as a significant mediator in the relationships between parental stress and positive parenting practices but not in the relationships of parental stress with parent?child conflict or the use of harsh discipline.

Convolutional neural network (CNN) model based on deep learning has excellent performance for target detection. However, the detection effect is poor when the object is circular or tubular because most of the existing object detection methods are based on the traditional rectangular box to detect and recognize objects. To solve the problem, we propose the circular representation structure and RepVGG module on the basis of CenterNet and expand the network prediction structure, thus proposing a high-precision and high-efficiency lightweight circular object detection method RebarDet. Specifically, circular tubular type objects will be optimized by replacing the traditional rectangular box with a circular box. Second, we improve the resolution of the network feature map and the upper limit of the number of objects detected in a single detect to achieve the expansion of the network prediction structure, optimized for the dense phenomenon that often occurs in circular tubular objects. Finally, the multibranch topology of RepVGG is introduced to sum the feature information extracted by different convolution modules, which improves the ability of the convolution module to extract information. We conducted extensive experiments on rebar datasets and used AB-Score as a new evaluation method to evaluate RebarDet. The experimental results show that RebarDet can achieve a detection accuracy of up to 0.8114 and a model inference speed of 6.9 fps while maintaining a moderate amount of parameters, which is superior to other mainstream object detection models and verifies the effectiveness of our proposed method. At the same time, RebarDet’s high precision detection of round tubular objects facilitates enterprise intelligent manufacturing processes.

No existing review has synthesized key questions about acculturation experiences among international migrant workers. This review aimed to explore (1) What are global migrant workers’ experiences with acculturation and acculturative stress? (2) What are acculturative stress coping strategies used by migrant workers? And (3) how effective are these strategies for migrant workers in assisting their acculturation in the host countries? Peer-reviewed and gray literature, without time limitation, were searched in six databases and included if the study: focused on acculturative stress and coping strategies; was conducted with international migrant workers; was published in English; and was empirical. Eleven studies met the inclusion criteria. Three-layered themes of acculturation process and acculturative stress were identified as: individual layer; work-related layer; and social layer. Three key coping strategies were identified: emotion-focused; problem-focused; and appraisal-focused. These coping strategies were used flexibly to increase coping effectiveness and evidence emerged that a particular type of acculturative stress might be solved more effectively by a specific coping strategy. Migrant workers faced numerous challenges in their acculturative process. Understanding this process and their coping strategies could be used in developing research and interventions to improve the well-being of migrant workers.

Hydrothermal activity on mid-ocean ridges is an important mechanism for the delivery of Zn from the mantle to the surface environment. Zinc isotopic fractionation during hydrothermal activity is mainly controlled by the precipitation of Zn-bearing sulfide minerals, in which isotopically light Zn is preferentially retained in solid phases rather than in solution during mineral precipitation. Thus, seafloor hydrothermal activity is expected to supply isotopically heavy Zn to the ocean. Here, we studied sulfide-rich samples from the Duanqiao-1 hydrothermal field, located on the Southwest Indian Ridge. We report that, at the hand-specimen scale, late-stage conduit sulfide material has lower δ66Zn values (−0.05 ± 0.15 ‰; n = 19) than early-stage material (+0.13 ± 0.15 ‰; n = 10). These lower values correlate with enrichments in Pb, As, Cd, and Ag, and elevated δ34S values. We attribute the low δ66Zn values to the remobilization of earlier sub-seafloor Zn-rich mineralization. Based on endmember mass balance calculations, and an assumption of a fractionation factor (αZnS-Sol.) of about 0.9997 between sphalerite and its parent solution, the remobilized Zn was found consist of about 1/3 to 2/3 of the total Zn in the fluid that formed the conduit samples. Our study suggests that late-stage subsurface hydrothermal remobilization may release isotopically-light Zn to the ocean, and that this process may be common along mid-ocean ridges, thus increasing the size of the previously identified isotopically light Zn sink in the ocean.

Text classification is an important topic in natural language processing, with the development of social network, many question-and-answer pairs regarding health-care and medicine flood social platforms. It is of great social value to mine and classify medical text and provide targeted medical services for patients. The existing algorithms of text classification can deal with simple semantic text, especially in the field of Chinese medical text, the text structure is complex and includes a large number of medical nomenclature and professional terms, which are difficult for patients to understand. We propose a Chinese medical text classification model using a BERT-based Chinese text encoder by N-gram representations (ZEN) and capsule network, which represent feature uses the ZEN model and extract the features by capsule network, we also design a N-gram medical dictionary to enhance medical text representation and feature extraction. The experimental results show that the precision, recall and F1-score of our model are improved by 10.25%, 11.13% and 12.29%, respectively, compared with the baseline models in average, which proves that our model has better performance.

The number of tourist attractions reviews, travel notes and other texts has grown exponentially in the Internet age. Effectively mining users’ potential opinions and emotions on tourist attractions, and helping to provide users with better recommendation services, which is of great practical significance. This paper proposes a multi-channel neural network model called Pre-BiLSTM combined with a pre-training mechanism. The model uses a combination of coarse and fine- granularity strategies to extract the features of text information such as reviews and travel notes to improve the performance of text sentiment analysis. First, we construct three channels and use the improved BERT and skip-gram methods with negative sampling to vectorize the word-level and vocabulary-level text, respectively, so as to obtain more abundant textual information. Second, we use the pre-training mechanism of BERT to generate deep bidirectional language representation relationships. Third, the vectors of the three channels are input into the BiLSTM network in parallel to extract global and local features. Finally, the model fuses the text features of the three channels and classifies them using SoftMax classifier. Furthermore, numerical experiments are conducted to demonstrate that Pre-BiLSTM outperforms the baselines by 6.27%, 12.83% and 18.12% in average in terms of accuracy, precision and F1-score.

To solve the problem of one-sided pursuit of the shortest distance but ignoring the tourist experience in the process of tourism route planning, an improved ant colony optimization algorithm is proposed for tourism route planning. Contextual information of scenic spots significantly effect people’s choice of tourism destination, so the pheromone update strategy is combined with the contextual information such as weather and comfort degree of the scenic spot in the process of searching the global optimal route, so that the pheromone update tends to the path suitable for tourists. At the same time, in order to avoid falling into local optimization, the sub-path support degree is introduced. The experimental results show that the optimized tourism route has greatly improved the tourist experience, the route distance is shortened by 20.5% and the convergence speed is increased by 21.2% compared with the basic algorithm, which proves that the improved algorithm is notably effective.

There are a large number of symptom consultation texts in medical and healthcare Internet communities, and Chinese health segmentation is more complex, which leads to the low accuracy of the existing algorithms for medical text classification. The deep learning model has advantages in extracting abstract features of text effectively. However, for a large number of samples of complex text data, especially for words with ambiguous meanings in the field of Chinese medical diagnosis, the word-level neural network model is insufficient. Therefore, in order to solve the triage and precise treatment of patients, we present an improved Double Channel (DC) mechanism as a significant enhancement to Long Short-Term Memory (LSTM). In this DC mechanism, two channels are used to receive word-level and char-level embedding, respectively, at the same time. Hybrid attention is proposed to combine the current time output with the current time unit state and then using attention to calculate the weight. By calculating the probability distribution of each timestep input data weight, the weight score is obtained, and then weighted summation is performed. At last, the data input by each timestep is subjected to trade-off learning to improve the generalization ability of the model learning. Moreover, we conduct an extensive performance evaluation on two different datasets: cMedQA and Sentiment140. The experimental results show that the DC-LSTM model proposed in this paper has significantly superior accuracy and ROC compared with the basic CNN-LSTM model.

Recently, a lot of Chinese patients consult treatment plans through social networking platforms, but the Chinese medical text contains rich information, including a large number of medical nomenclatures and symptom descriptions. How to build an intelligence model to automatically classify the text information consulted by patients and recommend the correct department for patients is very important. In order to address the problem of insufficient feature extraction from Chinese medical text and low accuracy, this paper proposes a dual channel Chinese medical text classification model. The model extracts feature of Chinese medical text at different granularity, comprehensively and accurately obtains effective feature information, and finally recommends departments for patients according to text classification. One channel of the model focuses on medical nomenclatures, symptoms and other words related to hospital departments, gives different weights, calculates corresponding feature vectors with convolution kernels of different sizes, and then obtains local text representation. The other channel uses the BiGRU network and attention mechanism to obtain text representation, highlighting the important information of the whole sentence, that is, global text representation. Finally, the model uses full connection layer to combine the representation vectors of the two channels, and uses Softmax classifier for classification. The experimental results show that the accuracy, recall and F1-score of the model are improved by 10.65%, 8.94% and 11.62% respectively compared with the baseline models in average, which proves that our model has better performance and robustness.

In view of the complex marine environment of navigation, especially in the case of multiple static and dynamic obstacles, the traditional obstacle avoidance algorithms applied to unmanned surface vehicles (USV) are prone to fall into the trap of local optimization. Therefore, this paper proposes an improved artificial potential field (APF) algorithm, which uses 5G communication technology to communicate between the USV and the control center. The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios. Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks, the algorithm introduces the concept of dynamic artificial potential field. For the multiple obstacles encountered in the process of USV sailing, based on the International Regulations for Preventing Collisions at Sea (COLREGS), the USV determines whether the next step will fall into local optimization through the discrimination mechanism. The local potential field of the USV will dynamically adjust, and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions. The objective function and cost function are designed at the same time, so that the USV can smoothly switch between the global path and the local obstacle avoidance. The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment, and take navigation time and economic cost into account.

The use of learning analytics (LA) in real-world educational applications is growing very fast as academic institutions realize the positive potential that is possible if LA is integrated in decision making. Education in schools on public health need to evolve in response to the new knowledge and th...

Objective. As the preclinical stage of Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI) is characterized by hidden onset, which is difficult to detect early. Traditional neuropsychological scales are main tools used for assessing MCI. However, due to its strong subjectivity and the influence of many factors such as subjects’ educational background, language and hearing ability, and time cost, its accuracy as the standard of early screening is low. Therefore, the purpose of this paper is to propose a new key technology of fast digital early warning for MCI based on eye movement objective data analysis. Methodology. Firstly, four exploratory indexes (test durations, correlation degree, lengths of gaze trajectory, and drift rate) of MCI early warning are determined based on the relevant literature research and semistructured expert interview; secondly, the eye movement state is captured based on the eye tracker to realize the data extraction of four exploratory indexes. On this basis, the human-computer interactive 2.5-minute fast digital early warning paradigm for MCI is designed; thirdly, the rationality of the four early warning indexes proposed in this paper and their early warning effectiveness on MCI are verified. Results. Through the small sample test of human-computer interactive 2.5 fast digital early warning paradigm for MCI conducted by 32 elderly people aged 70–90 in a medical institution in Hangzhou, the two indexes of “correlation degree” and “drift rate” with statistical differences are selected. The experiment results show that AUC of this MCI early warning paradigm is 0.824. Conclusion. The key technology of human-computer interactive 2.5 fast digital early warning for MCI proposed in this paper overcomes the limitations of the existing MCI early warning tools, such as low objectification level, high dependence on professional doctors, long test time, requiring high educational level, and so on. The experiment results show that the early warning technology, as a new generation of objective and effective digital early warning tool, can realize 2.5-minute fast and high-precision preliminary screening and early warning for MCI in the elderly.

Whilst after decades of research, exegetes have all agree on the complexity of Paul’s line of thinking in Rom 2:14–16, the ITC in its 2009 document, In Search of a Universal Ethic, still in an oversimplified manner propagates the view that Rom 2:14 presupposes a theory/theology of the natural law. This article makes plain the major disagreements among Pauline exegetes whether such presupposition stands by reviewing some major contributions to the discussion by raising major questions regarding the issue of φύσει in those verses, the nature of the law mentioned by Paul, the identity of the people Paul calls “Gentiles.” This article offers a more nuanced understanding of Rom 2:14. Keywords: Rom 2:14, ITC, Universal Ethic, Natural Law, φύσει, Gentiles