Other Worlds Than These Epub Converter
Globally, 80 billion pieces of new clothing are purchased each year, translating to $1.2 trillion annually for the global fashion industry. The majority of these products are assembled in China and Bangladesh while the United States consumes more clothing and textiles than any other nation in the world . Approximately 85 % of the clothing Americans consume, nearly 3.8 billion pounds annually, is sent to landfills as solid waste, amounting to nearly 80 pounds per American per year [2, 3].
Other Worlds Than These Epub Converter
The use of herbal remedies has also been widely embraced in many developed countries with complementary and alternative medicines (CAMs) now becoming mainstream in the UK and the rest of Europe, as well as in North America and Australia (Committee on the Use of Complementary, and Alternative Medicine by the American Public, Board on Health Promotion, and Disease Prevention, Institute of Medicine, 2005; Calapai, 2008; Braun et al., 2010; Anquez-Traxler, 2011). In fact, while places like the UK have a historical tradition of using herbal medicines (Nissen, 2010), the use is also widespread and well established in some other European countries (Calapai, 2008). In these developed countries, the most important among many other reasons for seeking herbal therapy is the belief that it will promote healthier living. Herbal medicines are, therefore, often viewed as a balanced and moderate approach to healing and individuals who use them as home remedies and over-the-counter drugs spend huge amount of money (in excess of billions of dollars) on herbal products. This explains in part the reason sales of herbal medicines are booming and represents a substantial proportion of the global drug market (Roberts and Tyler, 1997; Blumenthal et al., 1998; WHO, 2002a; Kong et al., 2003; Pal and Shukla, 2003; WHO, 2005a; Bandaranayake, 2006).
Providers of medicines, such as physicians, nurses, and pharmacists, often have little training in and understanding of how herbal medicines affect the health of their patients. Many of them are also poorly informed about these products and how they are being used. Adequate training is now very essential since most patients are almost often on other types of prescription or non-prescription medicines. In spite of the fact that the active involvement of orthodox healthcare professionals is continuously solicited and huge responsibility lies with them in terms of their valuable contributions to safety monitoring of medicinal products, it is also very important that all providers of herbal medicines are sufficiently empowered to play a role in monitoring safety of herbal medicines. This, however, should be in collaboration with the orthodox healthcare professionals. For this to be effective, it would be essential to create an atmosphere of trust to facilitate adequate sharing of knowledge about the use and safety of herbal medicines. In fact, the education of healthcare professionals, providers of herbal medicines, and patients/consumers is vital for the prevention of potentially serious risks from misuse of herbal medicines.
Evidence that people's attitudes to inequality and to policies that would reduce it can be influenced by quite straightforward interventions comes from research reported by McCall, Burk, Laperrière, and Richeson (2017). In three studies, these researchers show that exposing American participants to information about the rising economic inequality, compared to control information, led to stronger perceptions that economic success is due to structural factors rather than individual effort. In the largest of the three studies, involving a representative sample of American adults, it was also found that information about rising inequality led to greater endorsement of policies that could be implemented by government and by business to reduce inequality. This research shows that, under the right conditions, even those living in a society that is traditionally opposed to government intervention would support government policies to reduce inequality.
In fact, IoT is another big player implemented in a number of other industries including healthcare. Until recently, the objects of common use such as cars, watches, refrigerators and health-monitoring devices, did not usually produce or handle data and lacked internet connectivity. However, furnishing such objects with computer chips and sensors that enable data collection and transmission over internet has opened new avenues. The device technologies such as Radio Frequency IDentification (RFID) tags and readers, and Near Field Communication (NFC) devices, that can not only gather information but interact physically, are being increasingly used as the information and communication systems . This enables objects with RFID or NFC to communicate and function as a web of smart things. The analysis of data collected from these chips or sensors may reveal critical information that might be beneficial in improving lifestyle, establishing measures for energy conservation, improving transportation, and healthcare. In fact, IoT has become a rising movement in the field of healthcare. IoT devices create a continuous stream of data while monitoring the health of people (or patients) which makes these devices a major contributor to big data in healthcare. Such resources can interconnect various devices to provide a reliable, effective and smart healthcare service to the elderly and patients with a chronic illness .
Big data is the huge amounts of a variety of data generated at a rapid rate. The data gathered from various sources is mostly required for optimizing consumer services rather than consumer consumption. This is also true for big data from the biomedical research and healthcare. The major challenge with big data is how to handle this large volume of information. To make it available for scientific community, the data is required to be stored in a file format that is easily accessible and readable for an efficient analysis. In the context of healthcare data, another major challenge is the implementation of high-end computing tools, protocols and high-end hardware in the clinical setting. Experts from diverse backgrounds including biology, information technology, statistics, and mathematics are required to work together to achieve this goal. The data collected using the sensors can be made available on a storage cloud with pre-installed software tools developed by analytic tool developers. These tools would have data mining and ML functions developed by AI experts to convert the information stored as data into knowledge. Upon implementation, it would enhance the efficiency of acquiring, storing, analyzing, and visualization of big data from healthcare. The main task is to annotate, integrate, and present this complex data in an appropriate manner for a better understanding. In absence of such relevant information, the (healthcare) data remains quite cloudy and may not lead the biomedical researchers any further. Finally, visualization tools developed by computer graphics designers can efficiently display this newly gained knowledge.
Heterogeneity of data is another challenge in big data analysis. The huge size and highly heterogeneous nature of big data in healthcare renders it relatively less informative using the conventional technologies. The most common platforms for operating the software framework that assists big data analysis are high power computing clusters accessed via grid computing infrastructures. Cloud computing is such a system that has virtualized storage technologies and provides reliable services. It offers high reliability, scalability and autonomy along with ubiquitous access, dynamic resource discovery and composability. Such platforms can act as a receiver of data from the ubiquitous sensors, as a computer to analyze and interpret the data, as well as providing the user with easy to understand web-based visualization. In IoT, the big data processing and analytics can be performed closer to data source using the services of mobile edge computing cloudlets and fog computing. Advanced algorithms are required to implement ML and AI approaches for big data analysis on computing clusters. A programming language suitable for working on big data (e.g. Python, R or other languages) could be used to write such algorithms or software. Therefore, a good knowledge of biology and IT is required to handle the big data from biomedical research. Such a combination of both the trades usually fits for bioinformaticians. The most common among various platforms used for working with big data include Hadoop and Apache Spark. We briefly introduce these platforms below.
Apache Spark is another open source alternative to Hadoop. It is a unified engine for distributed data processing that includes higher-level libraries for supporting SQL queries (Spark SQL), streaming data (Spark Streaming), machine learning (MLlib) and graph processing (GraphX) . These libraries help in increasing developer productivity because the programming interface requires lesser coding efforts and can be seamlessly combined to create more types of complex computations. By implementing Resilient distributed Datasets (RDDs), in-memory processing of data is supported that can make Spark about 100 faster than Hadoop in multi-pass analytics (on smaller datasets) [19, 20]. This is more true when the data size is smaller than the available memory . This indicates that processing of really big data with Apache Spark would require a large amount of memory. Since, the cost of memory is higher than the hard drive, MapReduce is expected to be more cost effective for large datasets compared to Apache Spark. Similarly, Apache Storm was developed to provide a real-time framework for data stream processing. This platform supports most of the programming languages. Additionally, it offers good horizontal scalability and built-in-fault-tolerance capability for big data analysis.