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The COVID-19 pandemic has resulted in more than 440 million confirmed cases globally and almost 6 million reported deaths as of March 2022. Consequently, the world experienced grave repercussions to citizens' lives, health, wellness, and the economy. In responding to such a disastrous global event, countermeasures are often implemented to slow down and limit the virus's rapid spread. Meanwhile, disaster recovery, mitigation, and preparation measures have been taken to manage the impacts and losses of the ongoing and future pandemics. Data-driven techniques have been successfully applied to many domains and critical applications in recent years. Due to the highly interdisciplinary nature of pandemic management, researchers have proposed and developed data-driven techniques across various domains. However, a systematic and comprehensive survey of data-driven techniques for pandemic management is still missing. In this article, we review existing data analysis and visualization techniques and their applications for COVID-19 and future pandemic management with respect to four phases (namely, Response, Recovery, Mitigation, and Preparation) in disaster management. Data sources utilized in these studies and specific data acquisition and integration techniques for COVID-19 are also summarized. Furthermore, open issues and future directions for data-driven pandemic management are discussed. [ABSTRACT FROM AUTHOR]

From the start, the airline industry has remarkably connected countries all over the world through rapid long-distance transportation, helping people overcome geographic barriers. Consequently, this has ushered in substantial economic growth, both nationally and internationally. The airline industry produces vast amounts of data, capturing a diverse set of information about their operations, including data related to passengers, freight, flights, and much more. Analyzing air travel data can advance the understanding of airline market dynamics, allowing companies to provide customized, efficient, and safe transportation services. Due to big data challenges in such a complex environment, the benefits of drawing insights from the air travel data in the airline industry have not yet been fully explored. This article aims to survey various components and corresponding proposed data analysis methodologies that have been identified as essential to the inner workings of the airline industry. We introduce existing data sources commonly used in the papers surveyed and summarize their availability. Finally, we discuss several potential research directions to better harness airline data in the future.We anticipate this study to be used as a comprehensive reference for both members of the airline industry and academic scholars with an interest in airline research. [ABSTRACT FROM AUTHOR]

The gastrointestinal dysfunction and nutrient malabsorption associated with gastrointestinal tumors and related treatments may result in malnutrition in gastrointestinal (GI) cancer patients. Malnutrition has been associated with decreased treatment tolerance, poor quality of life, and increased mortality in this patient population. In this article, the relevant literature is reviewed and the factors, assessment tools, and management strategies related to malnutrition are summarized. In clinical care, healthcare providers should conduct early assessments of nutritional status and provide individualization nutritional counselling to promote treatment that effectively prolongs patient survival. This article may be used by nurses as a reference in caring for GI cancer patients. [ABSTRACT FROM AUTHOR]

This study developed a value-based scale measuring market orientation using both qualitative and quantitative research approaches. The shared views of both front-line employees and customers were used in the scale development process. Findings from the first stage of qualitative research resulted in a preliminary construct incorporating both concepts of market orientation and customer value. The following quantitative research steps validated the developed measures and supported the robustness of the proposed construct. [ABSTRACT FROM AUTHOR]

The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. The lack of core understanding renders these powerful methods as black-box machines that inhibit development at a fundamental level. Moreover, deep learning has repeatedly been perceived as a silver bullet to all stumbling blocks in machine learning, which is far from the truth. This article presents a comprehensive review of historical and recent state-of-theart approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications. It was also undertaken to review the issues faced in deep learning such as unsupervised learning, black-box models, and online learning and to illustrate how these challenges can be transformed into prolific future research avenues. [ABSTRACT FROM AUTHOR]

With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era. A vast amount of research work has been done in the multimedia area, targeting different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, very few research work provides a complete survey of the whole pine-line of the multimedia big data analytics, including the management and analysis of the large amount of data, the challenges and opportunities, and the promising research directions. To serve this purpose, we present this survey, which conducts a comprehensive overview of the state-of-the-art research work on multimedia big data analytics. It also aims to bridge the gap between multimedia challenges and big data solutions by providing the current big data frameworks, their applications in multimedia analyses, the strengths and limitations of the existing methods, and the potential future directions in multimedia big data analytics. To the best of our knowledge, this is the first survey that targets the most recent multimedia management techniques for very large-scale data and also provides the research studies and technologies advancing the multimedia analyses in this big data era. [ABSTRACT FROM AUTHOR]

Disasters, such as hurricanes, earthquakes and environmental emergencies, are serious disruptions of the functioning of a community or a society. To mitigate the social and physical impact of disasters, a critical task in disaster management is to extract situation updates on the disaster from a large number of disaster-related documents, and obtain a big picture of the disaster's trends and how it affects different areas. In this paper, we present a novel two-layer storyline generation framework which generates an overall storyline of the disaster events in the first layer, and provides condensed information about specific regions affected by the disaster (i.e., a location-specific storyline) in the second layer. To generate the overall storyline of a disaster, we consider both temporal and spatial factors, which are encoded using integer linear programming. While for location-specific sto- rylines, we employ a Steiner tree based method. Compared with the previous work of storyline generation, which generates at storylines without considering spatial information, our framework is more suitable for large-scale disaster events. We further demonstrate the efficacy of our proposed framework through the evaluation on the datasets of three major hurricane disasters. [ABSTRACT FROM AUTHOR]

Aims: The aims of this study of people with oral cavity cancer were to compare the social support, depression, nicotine dependence, physical function and social-emotional function of those who continued smoking with those who quit smoking, by matching age and survival time and to identify the predictors of continued smoking during the survival period. Background: People who continue to smoke after cancer treatment may have an impact on treatment response and survival. Design: A cross-sectional survey was conducted. Methods: This study compared 92 people with oral cavity cancer who continued smoking with 92 people who quit smoking, with matching for age and survival time between January 2015 - November 2015. Conditional logistic regression analysis was used to compare the two groups. Results: The quit smoking group had significantly more social support, less depression and greater social-emotional function than the continued smoking group. People who were unmarried, received surgery without reconstruction, had poor social support and had poor social-emotional function were more likely to continue smoking. Conclusions: People with oral cavity cancer were more likely to continue smoking after the treatment if they had low social support, depression, greater nicotine dependence and poor social-emotional function. Healthcare professionals should pay more attention to social support, psychological status and nicotine dependence of people who were treated for oral cavity cancer. [ABSTRACT FROM AUTHOR]

Metropolises offer ample employment opportunities, convenient facilities and a wide array of entertainment options. However large cities are also more vulnerable to natural disasters, which have caused widespread de- structions, claimed thousands of lives and left havoc for the survivors. Knowing which city is less susceptible to natural disasters is thus one of the most critical questions one faces when making decisions on travelling or job and business relocation. In this work, we propose a bipartite-graph based framework to compare the im- pacts of disasters on two cities by answering different queries using textual documents collected online. Besides intuitive simple comparisons using statistics, our system also generates textual comparative summaries to better describe the differences between the two cities in term of safety. Although a number of online services provide disaster events statistic information for cities, our framework compares the impacts of disasters on cities in a more straightforward and comprehensive way. [ABSTRACT FROM AUTHOR]

Improving disaster management and recovery techniques is one of national priorities given the huge toll caused by man-made and nature calamities. Data-driven disaster management aims at applying advanced data collection and analysis technologies to achieve more effective and responsive disaster management, and has undergone considerable progress in the last decade. However, to the best of our knowledge, there is currently no work that both summarizes recent progress and suggests future directions for this emerging research area. To remedy this situation, we provide a systematic treatment of the recent developments in datadriven disaster management. Specifically, we first present a general overview of the requirements and system architectures of disaster management systems and then summarize state-of-the-art data-driven techniques that have been applied on improving situation awareness as well as in addressing users' information needs in disaster management.We also discuss and categorize general data-mining and machine-learning techniques in disaster management. Finally, we recommend several research directions for further investigations. [ABSTRACT FROM AUTHOR]

Background: Anti-cancer chemotherapy may cause skin-toxicity reactions. Different types of cooling packages affect chemotherapy-induced skin toxicity reactions differently. Purpose: To evaluate the effects of cooling packing care on chemotherapy-induced skin toxicity reactions in cancer patients receiving chemotherapy. Methods: A systematic review approach was used. Searches were conducted in databases including Cochrane Library, Embase, MEDLINE, PubMed and Airiti Library using the keywords "chemotherapy cutaneous toxicity", "chemotherapy skin reaction", "chemotherapy skin toxicity", "frozen glove", "frozen sock", "cooling packaging care", "ice gloves", "ice socks", "usual care", "severity", "comfort", "satisfaction", "severity", and "comfort". The search focused on articles published before December 2016. Based on the inclusion and exclusion criteria, 5 articles involving relevant randomized controlled trials were extracted for review. Results: Elasto-Gel ice gloves or ice socks that were chilled to -25°C- -30°C and used for 15 mins during initial chemotherapy, for one hour during chemotherapy infusion, and for 15 mins after chemotherapy were shown to improve the frequency and severity of chemotherapy-induced skin toxicity reactions. Several studies were limited by small sample sizes and different types of cooling packing programs, temperature, timing, and frequency. Thus, further research is recommended to verify the effects of cooling packing care. Conclusions / Implications for Practice: Cancer patients who were treated with docetaxel or PLD and who used ice gloves or ice socks that were chilled to -25°C- -30°C for 15 mins during initial chemotherapy, for one hour during chemotherapy infusion, and for 15 mins after chemotherapy improved significantly in terms of the frequency and severity of their chemotherapy-induced skin toxicity reactions. Local cooling packing care is a non-pharmacotherapy approach that is low cost and free of side effects. This review is intended to provide a reference for clinical care. Key Words: cancer patients, chemotherapy, cooling packaging care, skin toxicity reaction, systematic review. [ABSTRACT FROM AUTHOR]

The snake algorithm has been proposed to solve many remote sensing and computer vision problems such as object segmentation, surface reconstruction, and object tracking. This paper introduces a framework for 3-D building model construction from LIDAR data based on the snake algorithm. It consists of nonterrain object identification, building and tree separation, building topology extraction, and adjustment by the snake algorithm. The challenging task in applying the snake algorithm to building topology adjustment is to find the global minima of energy functions derived for 2-D building topology. The traditional snake algorithm uses dynamic programming for computing the global minima of energy functions which is limited to snake problems with 1-D topology (i.e., a contour) and cannot handle problems with 2-D topology. In this paper, we have extended the dynamic programming method to address the snake problems with a 2-D planar topology using a novel graph reduction technique. Given a planar snake, a set of reduction operations is defined and used to simplify the graph of the planar snake into a set of isolated vertices while retaining the minimal energy of the graph. Another challenging task for 3-D building model reconstruction is how to enforce different kinds of geometric constraints during building topology refinement. This framework proposed two energy functions, deviation and direction energy functions, to enforce multiple geometric constraints on 2-D topology refinement naturally and efficiently. To examine the effectiveness of the framework, the framework has been applied on different data sets to construct 3-D building models from airborne LIDAR data. The results demonstrate that the proposed snake algorithm successfully found the global optima in polynomial time for all of the building topologies and generated satisfactory 3-D models for most of the buildings in the study areas. [ABSTRACT FROM PUBLISHER]

This study was to examine the effects of acupressure on fatigue and depression in HCC patients undergoing TACE. A quasiexperimental study design was used. Patients were evaluated at five time points: before treatment (T1) and 2, 3, 4, and 5 days after treating TACE (T2, T3, T4, and T5). Fatigue and depression were assessed by a VAS fatigue scale and a VAS depression scale at each time point. TFRS and BDI were administered at T1 and T5. Patients' fatigue and depression were significantly higher at T5 than at T1 in two groups. Fatigue and depression increased in both the experimental and control groups' patients over the five days of hospitalization during which TACE and chemotherapy were administered.The experimental group had significantly less fatigue than the control group, with lower subscale scores on physical, psychosocial, daily, and overall fatigue. There were no differences between the groups on depression. At posttest, the experimental group experienced lower physical, psychosocial, daily, and overall fatigue than the control group. Acupressure can improve fatigue in HCC patients during treatment with TACE but did not alleviate depression. Discharge planning should include home care for management of fatigue and depression. [ABSTRACT FROM AUTHOR]