The complexity of Free-Flood-It on 2xn boards

We consider the complexity of problems related to the combinatorial game Free-Flood-It, in which players aim to make a coloured graph monochromatic with the minimum possible number of flooding operations. Our main result is that computing the length of an optimal sequence is fixed parameter tractable (with the number of colours present as a parameter) when restricted to rectangular 2xn boards. We also show that, when the number of colours is unbounded, the problem remains NP-hard on such boards. This resolves a question of Clifford, Jalsenius, Montanaro and Sach (2010)

A survey of industrial recreation in fifty manufacturing concerns in the state of Massachusetts having over 1,000 employees

Thesis (Ed.M.)--Boston University, 1948. This item was digitized by the Internet Archive

Modeling and Control Techniques in Smart Systems

Energy and food crisis are two major problems that our human society has to face in the 21st century. With the world’s population reaching 7.62 billion as of May 2018, both electric power and agricultural industries turn to technological innovations for solutions to keep up the increasing demand. In the past and currently, utility companies rely on rule of thumb to estimate power consumption. However, inaccurate predictions often result in over production, and much energy is wasted. On the other hand, traditional periodic and threshold based irrigation practices have also been proven outdated. This problem is further compounded by recent years’ frequent droughts across the globe. New technologies are needed to manage irrigations more efficiently. Fortunately, with the unprecedented development of Artificial Intelligence (AI), wireless communication, and ubiquitous computing technologies, high degree of information integration and automation are steadily becoming reality. More smart metering devices are installed today than ever before, enabling fast and massive data collection. Patterns and trends can be more accurately predicted using machine learning techniques. Based on the results, utility companies can schedule production more efficiently, not only enhancing their profitabilities, but also making our world’s energy supply more sustainable. In addition, predictions can serve as references to detect anomalous activities like power theft and cyber attacks. On the other hand, with wireless communication, real-time soil moisture sensor readings and weather forecasts can be collected for precision irrigation. Smaller but more powerful controllers provide perfect platforms for complicated control algorithms. We designed and built a fully automated irrigation system at Bushland, Texas. It is designed to operate without any human intervention. Workers can program, move, and monitor multiple irrigation systems remotely. The algorithm that runs on the controls deserves more attention. AI and other state of art controlling techniques are implemented, making it much more powerful than any existing systems. By integrating professional crop yield simulation models like DSSAT, computers can run tens of thousand simulations on all kinds of weather and soil conditions, and more importantly, learn from the experience. In reality, such process would take thousands of years to obtain. Yet, the computers can find an optimum solution in minutes. The experience is then summarized as a policy and stored inside the controller as a lookup table. Furthermore, after each crop season, users can calibrate and update current policy with real harvest data. Crop yield models like DSSAT and AquaCrop play very important roles in agricultural research. They represent our best knowledge in plant biology and can be very accurate when well calibrated. However, the calibration process itself is often time consuming, thus limiting the scale and speed of using these models. We made efforts to combine different models to produce a single accurate prediction using machine learning techniques. The process does not require manual calibration, but only soil, historical weather, and harvest data. 20 models were built, and their results were evaluated and compared. With high accuracy, machine learning techniques have shown a promising direction to best utilize professional models, and demonstrated great potential for use in future agricultural research

Groundwater-surface water interaction in urban lowland catchments:Water quality dynamics in the city of Amsterdam

This research unravels the role of an overlooked source of nutrients in coastal low-lying urban settings: groundwater. In such hydrogeological conditions, for which the Amsterdam region serves as an example, drainage for urbanization has promoted the seepage of nutrient-rich groundwater into surface water. Groundwater is in these settings likely to outcompete other well-known sources such as urban runoff, agricultural inputs, treatment plant effluents, and leaking sewage systems. The water quality dynamics in such regions are determined by the variable interaction process between groundwater and surface water over space and time. Overload of nutrients causes surface water quality deterioration, e.g. low clarity, smelly water, fish kill, and loss of biodiversity due to the excessive growth of algae and plants. In some cases, harmful algae blooms even poses a serious risk to the health of humans, pets, and city wildlife. Substantial efforts have been made worldwide in abating nutrient overabundance in the natural and artificial water bodies. However, the formation of an effective strategy is seriously hampered by a lack of knowledge of the water quality dynamics and the underlying hydrological and hydrogeochemical processes. This research encompassed a novel multidisciplinary approach of studying the water quality dynamics over space and time, combining the regional hydrogeology, long term surface water and groundwater quality grab sampling data, and state-of-the-art high-resolution water quality monitoring technology. The time scales ranged from thousands of years of sedimentary geological conditions to temporal variations of water quality within an hour. We found that groundwater is the dominant source of nutrients in the study area, compromising the compliance of the water quality status required by the Water Framework Directive. The influences of the nutrient-rich groundwater on surface water quality were intensified by the installation of urban rain and groundwater drainage systems, and by maintaining low water levels by pumping which redistributes the nutrients from groundwater into the surrounding catchments. The flow paths of rain and groundwater were shortened due to the installation of the drainage systems bypassing the purification capacity of the subsurface redox transition zone. The mixing between the anoxic nutrient-rich groundwater and oxic rain water in the open water system performed as the major hydrological process determining the nutrient dynamics in the study catchment. Superposed on that, primary production was found to be the key process regulating N dynamics in spring and summer. Moreover, the sediment-water interface of the shallow surface waterways was found to fixate P during the growing season and releasing P when temperature dropped in the late autumn and winter. Based on this research, we recommended a change from the typical surveillance monitoring based management to a dynamic process-based management, aiming to mitigate the negative side effects of urbanization based on a better understanding of the governing processes

Smart Wireless Sensor Networks

The recent development of communication and sensor technology results in the growth of a new attractive and challenging area - wireless sensor networks (WSNs). A wireless sensor network which consists of a large number of sensor nodes is deployed in environmental fields to serve various applications. Facilitated with the ability of wireless communication and intelligent computation, these nodes become smart sensors which do not only perceive ambient physical parameters but also be able to process information, cooperate with each other and self-organize into the network. These new features assist the sensor nodes as well as the network to operate more efficiently in terms of both data acquisition and energy consumption. Special purposes of the applications require design and operation of WSNs different from conventional networks such as the internet. The network design must take into account of the objectives of specific applications. The nature of deployed environment must be considered. The limited of sensor nodes� resources such as memory, computational ability, communication bandwidth and energy source are the challenges in network design. A smart wireless sensor network must be able to deal with these constraints as well as to guarantee the connectivity, coverage, reliability and security of network's operation for a maximized lifetime. This book discusses various aspects of designing such smart wireless sensor networks. Main topics includes: design methodologies, network protocols and algorithms, quality of service management, coverage optimization, time synchronization and security techniques for sensor networks

Bowdoin Orient v.106, no.1-25 (1976-1977)

https://digitalcommons.bowdoin.edu/bowdoinorient-1970s/1007/thumbnail.jp

Natural Environment Management and Applied Systems Analysis

This volume contains papers from the NEMASA Konan-IIASA Joint Workshop on Natural Environment Management and Applied Systems Analysis, which took place at IIASA September 6-8, 2000. The workshop was an activity of the research project "Modeling by Computational Intelligence and its Application to Natural Environment Management." The project is being supported by the Hirao Taro Foundation of the Konan University Association for Academic Research, Kobe, Japan. The management of the natural environment -- in particular, the use of advanced agricultural practices -- poses a major challenge to modern society, but perhaps applied systems analysis can help. The workshop set was about to: present new concepts and methodologies for managing the environment, and offer an open forum for the exchange of ideas among research disciplines, especially between agro-environmental and applied systems analysis research and between researchers and practitioners. The paper deal with a range of topics. The editors have arranged them into the following categories: (1) modeling methodologies, (2) data analysis, (3) land use, (4) water management, and (5) applications