The Effects of War and Migration Trauma on Southeast Asian Families in the United States

This review article focuses on Southeast Asian (SEA) families, which include Vietnamese, Laotian, Hmong, and Cambodian ethnic groups, comprising about three million people in the United States. Although many differences exist among SEAs, they share experiences of war and migration-related trauma and losses that continue to have long-term effects on their families and individual well-being within and across generations. Research and practice work with SEA families and individuals requires in depth knowledge of their experiences before, during, and after migration to the U.S. This article on SEA families, although not exhaustive in its coverage, highlights the following topics: SEA populations in the U.S., migration history, resettlement and adaptation in the U.S., mental health issues of SEAs, traditional SEA family, migration and family formation, migration and family relationships, migration and family in later life, and implications for research and practice with SEA families and individuals

Differential diagnosis of dna viruses related to reproductive disorder on sows by multiplex-pcr technique

The newly emerged diseases caused by ASFV and PCV3 and their confirmed prevelance in Vietnam whereas most of available common commercial methods such as ELISA or realtime PCR designed for detecting single pathogen per reaction, highlighted a necessity for another diagnostic method to simultaneously detect and differentiate DNA viruses that are related to reproductive failures in sow herds including PCV2, PCV3, PPV, ASFV. In this communication, a diagnostic multiplex-PCR (mPCR) was established with pathogen-specific primers selected from previous studies and another set of primers designed for COX1 gene serving as an internal amplification control (IAC). The predicted products of PCV2, PCV3, PPV, ASFV and IAC were 702 bp, 223 bp, 380 bp, 278 bp and 463 bp, respectively. After optimization, the mPCR functioned specifically at 62°C. Results revealed the consistent detection limit at 100 copies/gene/reaction. In application, 185 serum samples from sows were used to examine the presence of the related pathogens. mPCR results showed that the mono-infection rate of PCV2, PCV3, PPV, and ASFV was 0% (0/185), 40% (74/185), 28.1% (52/185), and 48.1% (89/185), respectively. Regarding coinfection rate, the data indicated that coinfections of 2, 3 and 4 pathogens were 20%, 8.1% and 0% accordingly. In conclusion, the mPCR assay was successfully established and ready to serve for diagnosis of PCV2, PCV3, PPV and ASFV infection in reality with high specificity and sensitivity. It is a good contribution to a better understanding of the epidemiology of these diseases in swine

Presence of e-EDCs in surface water and effluents of pollution sources in Sai Gon and Dong Nai river basin

© 2016 This study aimed to assess the presence of estrogenic endocrine disrupting compounds (e-EDCs) including estriol, bisphenol A (BPA), atrazine (ATZ), octylphenol, octylphenol diethoxylate, octylphenol triethoxylate, nonylphenol, Nonylphenol triethoxylate (NPE3), nonylphenol diethoxylate (NPE2) and 17β-estradiol in: (i) Sai Gon and Dong Nai river waters which have been major raw water sources for drinking water supply for Ho Chi Minh City (HCMC) and neighbouring provinces, and (ii) water pollution sources located in their catchment basin. NPE3 and NPE2 were detected in most of the surface water samples. Concentrations of NPE3 were in a range of less than 5.9–235 ng L−1, whereas BPA was detected at significantly high concentrations in the dry season in canals in HCMC. In the upstream of Sai Gon and Dong Nai Rivers, ATZ concentrations were observed at water intake of water treatment plants served for HCMC water supply system. Similarly, high potential risk of NPE2 and NPE3 contamination at Phu Cuong Bridge near Hoa Phu water intake was identified. The significant correlation between NPE2, dissolved organic carbon and total nitrogen was found. Estrogenic equivalent or estrogenic activity of Sai Gon and Dong Nai Rivers was lower than those of the previous studies. Compared with other studies, e-EDCs of pollution in Sai Gon river basin were relatively low

Utilization of services provided by village based ethnic minority midwives in mountainous villages of Vietnam

Introduction: Since 2011, the Vietnam’s Ministry of Health implemented the ethnic minority midwives (EMMs) scheme in order to increase the utilization of maternal health services by women from ethnic minorities and those living in hard-to-reach mountainous areas. This paper analyzes the utilization of antenatal, delivery, and postpartum care provided by EMMs and reports the key determinants of utilization of EMM services as perceived by service users. Methods: A structured questionnaire was administered in 2015 to all mothers (n=320) who gave birth to a live-born during a 1-year period in 31 villages which had EMM in two provinces, Dien Bien and Kon Tum. A multivariate logistic regression model was used to examine the association between all potential factors and the use of services provided by EMMs. Results: We found that EMMs provided more antenatal care and postnatal care as compared with delivery services, which corresponded to their job descriptions. The results also showed that utilization of antenatal care provided by EMMs was lower than that of postnatal care. The proportion of those who never heard about EMM was high (24%). Among the mothers who knew about EMM services, 33.4% had antenatal checkups, 20.1% were attended during home deliveries, and 57.3% had postnatal visits by an EMM. Key factors that determined the use of EMM services included knowledge of the location of EMM’s house, being aware about EMMs by health workers, trust in services provided by EMMs, and perception that many others mothers in a village also knew about EMM services. Conclusion: EMM seems to be an important mechanism to ensure assistance during home births and postnatal care for ethnic minority groups, who are often resistant to attend health facilities. Building trust and engaging with communities are the key facilitators to increase the utilization of services provided by EMMs. Communication campaigns to raise awareness about EMMs and to promote their services in the village, particularly by other health workers, represent an important strategy to further improve effectiveness of EMM scheme

Marketing intelligence from data mining perspective : A literature review

The digital transformation enables enterprises to mine big data for marketing intelligence on markets, customers, products, and competitor. However, there is a lack of a comprehensive literature review on this issue. With an aim to support enterprises to accelerate the digital transformation and gain competitive advantages through exploiting marketing intelligence from big data, this paper examines the literature in the period from 2001–2018. Consequently, 76 most relevant articles are analyzed based on four marketing intelligence components (Markets, Customers, Products, and Competitors) and six data mining models (Association, Classification, Clustering, Regression, Prediction, and Sequence Discovery). The findings of this study indicate that the research area of product and customer intelligence receives most research attention. This paper also provides a roadmap to guide future research on bridging marketing and information systems through the application of data mining to exploit marketing intelligence from big data

Unveiling the role of artificial intelligence for wound assessment and wound healing prediction

Wound healing is a very dynamic and complex process as it involves the patient, wound-level parameters, as well as biological, environmental, and socioeconomic factors. Its process includes hemostasis, inflammation, proliferation, and remodeling. Evaluation of wound components such as angiogenesis, inflammation, restoration of connective tissue matrix, wound contraction, remodeling, and re-epithelization would detail the healing process. Understanding key mechanisms in the healing process is critical to wound research. Elucidating its healing complexity would enable control and optimize the processes for achieving faster healing, preventing wound complications, and undesired outcomes such as infection, periwound dermatitis and edema, hematomas, dehiscence, maceration, or scarring. Wound assessment is an essential step for selecting an appropriate treatment and evaluating the wound healing process. The use of artificial intelligence (AI) as advanced computer-assisted methods is promising for gaining insights into wound assessment and healing. As AI-based approaches have been explored for various applications in wound care and research, this paper provides an overview of recent studies exploring the application of AI and its technical developments and suitability for accurate wound assessment and prediction of wound healing. Several studies have been done across the globe, especially in North America, Europe, Oceania, and Asia. The results of these studies have shown that AI-based approaches are promising for wound assessment and prediction of wound healing. However, there are still some limitations and challenges that need to be addressed. This paper also discusses the challenges and limitations of AI-based approaches for wound assessment and prediction of wound healing. The paper concludes with a discussion of future research directions and recommendations for the use of AI-based approaches for wound assessment and prediction of wound healing

A knowledge-based framework for developing smart interfaces for smart service systems

Nowadays, smart service systems are value co-creating configurations of people, technologies, organisations,and information that are capable of in-dependent learning, adaptation, and decision-making. They are propelled byunprecedented advancements in connectivity, sensors, data storage, computation, and artificial intelligence. One of thekey challenges faced by those systems is how to provide smart interfaces, which can assist business users with limitedknowledge in business analytics in gaining business insights from business data. For this reason, this paper proposes aknowledge-based framework for developing smart interfaces for smart service systems, which will assist business usersin exploring business data to gain business in-sights and subsequently make better business decisions to promote valueco-creation. A prototype with simulation data has been developed and presented as a running example to illustrate how theproposed framework can be applied to create an effective smart interface for a typical smart service system: a customer intelligence system

Federated Deep Reinforcement Learning-based Bitrate Adaptation for Dynamic Adaptive Streaming over HTTP

In video streaming over HTTP, the bitrate adaptation selects the quality of video chunks depending on the current network condition. Some previous works have applied deep reinforcement learning (DRL) algorithms to determine the chunk's bitrate from the observed states to maximize the quality-of-experience (QoE). However, to build an intelligent model that can predict in various environments, such as 3G, 4G, Wifi, \textit{etc.}, the states observed from these environments must be sent to a server for training centrally. In this work, we integrate federated learning (FL) to DRL-based rate adaptation to train a model appropriate for different environments. The clients in the proposed framework train their model locally and only update the weights to the server. The simulations show that our federated DRL-based rate adaptations, called FDRLABR with different DRL algorithms, such as deep Q-learning, advantage actor-critic, and proximal policy optimization, yield better performance than the traditional bitrate adaptation methods in various environments.Comment: 13 pages, 1 colum

Towards a service-oriented architecture for knowledge management in big data era

Nowadays, big data is a revolution that transforms conventional enterprises into data-driven organizations in which knowledge discovered from big data will be integrated into traditional knowledge to improve decision-making and to facilitate organizational learning. Consequently, a major concern is how to evolve current knowledge management systems, which are confronted with a various and unprecedented amount of data, resulting from different data sources. Therefore, a new generation of knowledge management systems is required for exploring and exploiting big data as well as for facilitating the knowledge co-creation between the society and its business environment to foster innovation. This article proposes a service-oriented architecture for elaborating a new generation of big data-driven knowledge management systems to help enterprises to promote knowledge co-creation and to obtain more business value from big data. The proposed architecture is presented based on the principles of design science research and its evaluation uses the analytical evaluation method

The quest for customer intelligence to support marketing decisions: A knowledge-based framework

The quest for customer intelligence to create value in marketing has highlighted the significance of the research focus of this paper. Customer intelligence, which is defined as understandings or insights resulting from the application of analytic techniques, plays a significant role in the survival and prosperity of enterprises in the knowledge-based economy. In this light, the paper has developed a framework of customer intelligence to support marketing decisions through the lens of knowledge-based theory. The proposed framework aims at supporting enterprises to identify the right customer data for the right customer intelligence corresponding with the right marketing decisions. In this light, four types of customer intelligence are clarified including product-aware intelligence, customer DNA intelligence, customer experience intelligence, and customer value intelligence. The applications of customer intelligence are also elucidated with relevant marketing decisions to maximize value creation. To illustrate the framework, an example is presented. The importance and originality of this study are that it responds to changes in customer intelligence in the age of massive data and covers multifaced aspects of marketing decisions