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Model-driven software engineering in practice
This book discusses how model-based approaches can improve the daily practice of software professionals. This is known as Model-Driven Software Engineering (MDSE) or, simply, Model-Driven Engineering (MDE). MDSE practices have proved to increase efficiency and effectiveness in software development, as demonstrated by various quantitative and qualitative studies. MDSE adoption in the software industry is foreseen to grow exponentially in the near future, e.g., due to the convergence of software development and business analysis. The aim of this book is to provide you with an agile and flexible tool to introduce you to the MDSE world, thus allowing you to quickly understand its basic principles and techniques and to choose the right set of MDSE instruments for your needs so that you can start to benefit from MDSE right away.
Book
The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods
State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.
Journal Article
Classic Gatwick propliners
\"When Ronald Waters bought 90 acres of farmland adjacent to the Gatwick racecourse in 1930 in order to set up a private airfield, little did he know how that airfield would evolve over the next eighty-nine years to become the world's busiest single-runway airport. Back in the 1960s and '70s it became a hub for aircraft enthusiasts and photographers who, thanks to the viewing decks, could get up close to the aircraft and enjoy the eclectic mix of new jetliners and old propeller airliners. Tom Singfield, ex-Gatwick Air Traffic Controller and a fan of all classic airliners, has long dreamt of a book showcasing the glory days of Gatwick's classic airliners. After thirty years of searching out the very best colour images of that time, he is now able to publish the results of his searches in this book. These stunning pictures celebrate those wonderful times and the amazing and much missed 'propliners' that operated from Gatwick for the first twenty years after its reopening in 1958\"--Publisher description.
Book
Demand Driven Material Requirements Planning (DDMRP): A systematic review and classification
Purpose: Demand Driven Material Requirements Planning (DDMRP) aims to deal with variability by adjusting inventory levels while maintaining, or even increasing, customer service levels. This approach bridges the push and pull approaches. Even though it first made its appearance in 2011, research in this field remains relatively limited. This paper aims to measure the spatiotemporal evolution of the DDMRP, its scope and context of implementation, and the research lines studied in that field in order to identify areas that still need to be addressed by future researchers. Design/methodology/approach: The systematic literature review approach adopted in this paper examines research dealing with the DDMRP approach published in different languages between 2009 and 2020. To-date papers focused on the performance analysis and comparison, what differentiates this study is the focus on the scientific evolution level of DDMRP, the parameters, and contexts that should be more studied. Findings: The results show that DDMRP is not yet a mature method and that the robustness of the approach still needs to be tested. More research is also required to determine scientifically some setting parameters, how the proposed DDMRP could be implemented in different industrial contexts with existing information systems. Originality/value: Based on the evolution analysis of DDMRP, this study outlines its current state of maturity and its different shortcomings under a broader vision to make this method more complete on the scientific and industrial level.
Journal Article
Aspect-oriented, model-driven software product lines : the AMPLE way
\"Software product lines provide a systematic means of managing variability in a suite of products. They have many benefits but there are three major barriers that can prevent them from reaching their full potential. First, there is the challenge of scale: a large number of variants may exist in a product line context and the number of interrelationships and dependencies can rise exponentially. Second, variations tend to be systemic by nature in that they affect the whole architecture of the software product line. Third, software product lines often serve different business contexts, each with its own intricacies and complexities. The AMPLE (http://www.ample-project.net/) approach tackles these three challenges by combining advances in aspect-oriented software development and model-driven engineering. The full suite of methods and tools that constitute this approach are discussed in detail in this edited volume and illustrated using three real-world industrial case studies\"-- Provided by publisher.
Book
The emerging data–driven Smart City and its innovative applied solutions for sustainability: the cases of London and Barcelona
The big data revolution is heralding an era where instrumentation, datafication, and computation are increasingly pervading the very fabric of cities. Big data technologies have become essential to the functioning of cities. Consequently, urban processes and practices are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for smart cities. Such form is increasingly being directed towards tackling the challenges of sustainability in the light of the escalating urbanization trend. This paper investigates how the emerging data-driven smart city is being practiced and justified in terms of the development and implementation of its innovative applied solutions for sustainability. To illuminate this new urban phenomenon, a descriptive case study is adopted as a qualitative research methodology to examine and compare London and Barcelona as the leading data-driven smart cities in Europe. This study shows that these cities have a high level of the development of applied data-driven technologies, but they slightly differ in the level of the implementation of such technologies in different city systems and domains with respect to sustainability areas. They also moderately differ in the degree of their readiness as to the availability and development level of the competences and infrastructure needed to generate, transmit, process, and analyze large masses of data to extract useful knowledge for enhanced decision making and deep insights pertaining to urban operational functioning, management, and planning in relation to sustainability. London takes the lead as regards the ICT infrastructure and data sources, whereas Barcelona has the best practices in the data-oriented competences, notably horizontal information platforms, operations centers, dashboards, training programs and educational institutes, innovation labs, research centers, and strategic planning offices. This research enhances the scholarly community’s current understanding of the new phenomenon of the data-driven city with respect to the untapped synergic potential of the integration of smart urbanism and sustainable urbanism for advancing sustainability in the light of the emerging paradigm of big data computing. No previous work has, to the best of our knowledge, explored and highlighted the link between the data-driven smart solutions and the sustainable development strategies in the context of data-driven sustainable smart cities as a new paradigm of urbanism.
Journal Article
Sick to debt : how smarter markets lead to better care
An informed argument for reworking the broken market-based U.S. healthcare system by making cost and quality more transparent. The United States has the most expensive healthcare system in the world. While policy makers have argued over who is at fault for this, the system has been quietly moving toward high-deductible insurance plans that require patients to pay large amounts out of pocket before insurance kicks in. The idea behind this shift is that patients will become better consumers of healthcare when forced to pay for their medical expenses. Laying bare the perils of the current situation, Peter A. Ubel, a physician and behavioral scientist, notes that even when patients have time to shop around, healthcare costs remain largely opaque, difficult to access, and hard to compare. Arguing for a middle path between a market-based and a completely free system, Ubel envisions more transparent, smarter healthcare plans that tie the prices of treatments to the value they provide so that people can afford to receive the care they deserve.
Book
Human papillomavirus‐driven head and neck cancers in Japan during 2008–2009 and 2018–2019: The BROADEN study
There is limited understanding of epidemiology and time trends of human papilloma virus (HPV)‐driven head and neck cancers (HNC) in Japan, especially outside of the oropharynx. To assess HPV‐driven HNC, a non‐interventional study (BROADEN) of HNC patients diagnosed in 2008–2009 and 2018–2019 was conducted in Japan. Adult patients with oropharyngeal, nasopharyngeal, laryngeal, hypopharyngeal or oral cavity cancers were included in this study. HPV was centrally tested using p16INK4a immunohistochemistry, HPV‐DNA PCR and HPV E6*I mRNA. HPV attributability required positivity in at least two tests (p16INK4a immunohistochemistry, HPV‐DNA PCR, HPV E6*I mRNA) in the oropharynx, and HPV‐DNA and HPV E6*I mRNA positivity for non‐oropharynx sites. Nineteen hospitals included a total of 1108 patients, of whom 981 had valid samples. Men accounted for 82% of HNC diagnoses. Patients in the earlier cohort were younger and included a higher percentage of smokers. There was an increasing trend of HPV‐driven oropharyngeal cancer over the last decade, from 44.2% to 51.7%. HPV attribution in nasopharyngeal cancers was 3.2% in 2008–2009 and 7.5% in 2018–2019; and 4.4% and 0% for larynx respectively. In total, 95.2% of HPV‐driven HNC were attributed to HPV genotypes included in the 9‐valent HPV vaccine being HPV16 the most prominent genotype. These results suggest that an epidemiologic shift is happening in Japan, with a decrease in smoking and alcohol use and an increase in HPV‐driven HNC. The increasing trend of HPV‐driven HNC in Japan highlights the need for preventive strategies to mitigate the rise of HPV‐driven HNC. The BROADEN study is one of the largest observational studies conducted in Japan assessing human papilloma virus (HPV) in head and neck cancer (HNC). The study includes 1108 patients diagnosed in 2008–2009 and 2018–2019 across 19 hospitals. Overall 981 HNC samples were tested by HPV‐DNA PCR, p16INK4a, and HPV E6*I mRNA in a central laboratory using strict quality control processes for all laboratory steps. This study demonstrates an increasing trend of HPV attributability in oropharyngeal cancer over this 10‐year period, from 44.2% in 2008–2009 to 51.7% in 2018–2019 reflecting an annual percentage change (APC) of 1.58%, as well as in nasopharyngeal cancers from 3.2% to 7.5% representing an APC of 8.92%. Men accounted for 82% of HNC diagnoses, and patients in the earlier cohort were younger and had a higher percentage of smokers. Notably, the study found that 94.6% of these HPV‐driven HNC cases were attributed to HPV genotypes included in the prophylactic 9‐valent HPV vaccine highlighting the value of potential preventive strategies.
Journal Article
On Some Limitations of Current Machine Learning Weather Prediction Models
Machine Learning (ML) is having a profound impact in the domain of Weather and Climate Prediction. A recent development in this area has been the emergence of fully data‐driven ML prediction models which routinely claim superior performance to that of traditional physics‐based models. We examine some aspects of the forecasts produced by three of the leading current ML models, Pangu‐Weather, FourCastNet and GraphCast, with a focus on their fidelity and physical consistency. The main conclusion is that these ML models are not able to properly reproduce sub‐synoptic and mesoscale weather phenomena and lack the fidelity and physical consistency of physics‐based models and this has impacts on the interpretation of their forecasts and their perceived skill. Balancing forecast skill and physical realism will be an important consideration for future ML models. Plain Language Summary The last few years have seen the emergence of a new type of weather forecasting models completely based on ML technologies. These models do not codify the physical laws governing atmospheric dynamics but learn to produce forecasts from historical reanalysis data sets of the Earth system like the ECMWF ERA5. In this work we show that the forecasts produced by some of the leading ML models are physically inconsistent and should be better considered as post‐processing algorithms rather than realistic simulators of the atmosphere. The challenge for next generation of ML models for weather forecasting will be to improve their fidelity while maintaining forecast skill. Key Points Forecasts from Machine Learning (ML) models have energy spectra notably different from those of their training reanalysis fields and Numerical Weather Prediction models This results in overly smooth predictions and weather phenomena at spatial scales shorter than 300–400 km are not properly represented Fundamental physical balances and derived quantities are not realistically represented in the forecasts of the ML models
Journal Article
Does data-driven culture impact innovation and performance of a firm? An empirical examination
Data-driven culture is considered to bring business-oriented cultural transformation to a firm. It is considered to provide substantial dividends to the firms’ product and process innovations. Recently, several firms have been using different advanced technology-embedded business analytics (BA) tools to improve their business performance. Again, advancement of information and communication technology has helped firms to explore the option to use BA tools with artificial intelligence. This has brought radical change in the business-oriented cultural landscape of the firms to arrive at accurate decision-making to improve their innovation and performance. In this perspective, the aim of this study is to show how a firm’s data-driven culture impacts its product and process innovation, which in turn improves its performance and provides better competitive advantage in the current business environment. With the help of background study, a resource-based view model and different theories, a conceptual model has been developed. The conceptual model has been validated with 456 usable responses from the employees of different firms using different business analytics tools. The study highlights that data-driven culture highly influences both product and process innovation, making the firm more competitive in the industry. In this study, leadership support and data-driven culture have been taken as moderators, whereas firm size, firm age and industry type have been taken as control variables.
Journal Article