Students will use a perceived weak material to construct something that is surprisingly strong.
Students can experiment with different shapes and configurations to see what holds the most weight.
The cube size is defined, what each student places within each 4x4 square, is up to them.
Students will use a perceived weak material to construct something that is surprisingly strong.
Students can experiment with different shapes and configurations to see what holds the most weight.
The cube size is defined, what each student places within each 4x4 square, is up to them.
This course, AI in Practice: Preparing for AI, is the 1st course of the online education program AI in Practice. The course gives you a kaleidoscope of examples of applications of AI in various organizations, outlines the state of the art in modern AI research, and provides practical tools for integrating AI into your own organization. The program AI in Practice is built from two initial courses, AI in Practice: Preparing for AI and AI in Practice: Applying AI.
The AI in Practice: Preparing for AI course is designed for people who want to apply AI in their own practical situation.
For the experienced manager who wants to know what AI can do for her own organization.
For the data analyst or business consultant who wants to understand how AI can be applied in the business processes of the company for which they work.
For the student who wants to understand how the results of AI research can be translated into practical applications.
For students interested in studying biomechanical engineering, especially in the field of surgery, this lesson serves as an anatomy and physiology primer of the abdominopelvic cavity. Students are introduced to the abdominopelvic cavity—a region of the body that is the focus of laparoscopic surgery—as well as the benefits and drawbacks of laparoscopic surgery. Understanding the abdominopelvic environment and laparoscopic surgery is critical for biomechanical engineers who design laparoscopic surgical tools.
Students learn about the concepts of accuracy and approximation as they pertain to robotics, gain insight into experimental accuracy, and learn how and when to estimate values that they measure. Students also explore sources of error stemming from the robot setup and rounding numbers.
The course treats the following topics: - Relevant physical oceanography - Elements of marine geology (seafloor topography, acoustical properties of sediments and rocks) - Underwater sound propagation (ray acoustics, ocean noise) - Interaction of sound with the seafloor (reflection, scattering) - Principles of sonar (beamforming) - Underwater acoustic mapping systems (single beam echo sounding, multi-beam echo sounding, sidescan sonar) - Data analysis (refraction corrections, digital terrain modelling) - Applications (hydrographic survey planning and navigation, coastal engineering) - Current and future developments.
How can you use the Engineering Design Process to access a geographically inaccessible location to deliver supplies?
This article describes covert and overt active engagement strategies for use with elementary students. Find the Question templates are included for use with informational text.
This activity first asks the students to study the patterns of bird flight and understand that four main forces affect the flight abilities of a bird. They will study the shape, feather structure, and resulting differences in the pattern of flight. They will then look at several articles that feature newly designed planes and the birds that they are modeled after. The final component of this activity is to watch the Nature documentary, "Raptor Force" which chronicles the flight patterns of birds, how researchers study these animals, and what interests our military and aeronautical engineers about these natural adaptations. This activity serves as an extension to the biomimetics lesson. Although students will not be using this information in the design process for their desert resort, it provides interesting information pertaining to the current use of biomimetics in the field of aviation. Students may extend their design process by using this information to create a means of transportation to and from the resort if they chose to.
The 16 lectures in this course cover the topics of adaptive antennas and phased arrays. Both theory and experiments are covered in the lectures. Part one (lectures 1 to 7) covers adaptive antennas. Part two (lectures 8 to 16) covers phased arrays. Parts one and two can be studied independently (in either order). The intended audience for this course is primarily practicing engineers and students in electrical engineering. This course is presented by Dr. Alan J. Fenn, senior staff member at MIT Lincoln Laboratory.
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This course offers analysis and practice of various forms of scientific and technical writing, from memos to journal articles, in addition to strategies for conveying technical information to specialist and non-specialist audiences. Comparable to 21W.780 Communicating in Technical Organizations, but methods in this course are designed to deal with special problems of advanced ELS or bilingual students. The goal of the workshop is to develop effective writing skills for academic and professional contexts. Models, materials, topics and assignments vary from term to term.
Disseminates documented innovations in engineering education practice through the creative use of multimedia. includes descriptions of innovative curricula, courses, and teaching practices both within and outside the classroom that are clearly built upon a foundation of accepted learning science principles. Completed and documented studies are published as full articles; work in progress that shows distinct promise of eventual success may be published as educational briefs.
Our brains control every movement we make. Most of us take for granted our ability to pick up a cup or change the television station. However, for people who have lost a limb or become paralyzed, the inability to do these things means a loss of freedom and independence. This video segment from Greater Boston describes how neuroscientists and bioengineers have teamed up to create a system that allows people who have lost motor functions to control electronic devices through their thoughts alone. Grades 6-12
In this lesson, students learn about work as defined by physical science and see that work is made easier through the use of simple machines. Already encountering simple machines everyday, students will be alerted to their widespread uses in everyday life. This lesson serves as the starting point for the Simple Machines Unit.
Our human society consists of many intertwined Large Scale Socio-Technical Systems (LSSTS), such as infrastructures, industrial networks, the financial systems etc. Environmental pressures created by these systems on EarthŰŞs carrying capacity are leading to exhaustion of natural resources, loss of habitats and biodiversity, and are causing a resource and climate crisis. To avoid this sustainability crisis, we urgently need to transform our production and consumption patterns. Given that we, as inhabitants of this planet, are part of a complex and integrated global system, where and how should we begin this transformation? And how can we also ensure that our transformation efforts will lead to a sustainable world? LSSTS and the ecosystems that they are embedded in are known to be Complex Adaptive Systems (CAS). According to John Holland CAS are "...a dynamic network of many agents (which may represent cells, species, individuals, firms, nations) acting in parallel, constantly acting and reacting to what the other agents are doing. The control of a CAS tends to be highly dispersed and decentralized. If there is to be any coherent behavior in the system, it will have to to arise from competition and cooperation among the agents themselves. The overall behavior of the system is the result of a huge number of decisions made every moment" by many individual agents. Understanding Complex Adaptive Systems requires tools that themselves are complex to create and understand. Shalizi defines Agent Based Modeling as "An agent is a persistent thing which has some state we find worth representing, and which interacts with other agents, mutually modifying each otherŰŞs states. The components of an agent-based model are a collection of agents and their states, the rules governing the interactions of the agents and the environment within which they live." This course will explore the theory of CAS and their main properties. It will also teach you how to work with Agent Based Models in order to model and understand CAS.
Do you need proof that driving is a dangerous activity? More Americans have died in car crashes over the past 100 years than in all the wars the U.S. has ever fought combined. More than 40,000 Americans die each year on the nation's highways, most as the result of high-speed collisions. In this video segment adapted from NOVA, learn how engineers developed the air bag, an important automobile-safety device now found in most cars.
Recommended for: Grades 3-12
In this video segment adapted from ZOOM, cast members make their own hovercraft and demonstrate how the air leaking out of a balloon can make a plastic plate hover above a table.
This activity introduces students to high precision GPS as it is used in geoscience research. Students build "gumdrop" GPS units and study data from three Alaska GPS stations from the Plate Boundary Observatory network. They learn how Alaska's south central region is "locked and loading" as the Pacific Plate pushes into North America and builds up energy that will be released in the future in other earthquakes such as the 1964 Alaska earthquake.
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This is a research-oriented course on algorithm engineering, which will cover both the theory and practice of algorithms and data structures. Students will learn about models of computation, algorithm design and analysis, and performance engineering of algorithm implementations. We will study the design and implementation of sequential, parallel, cache-efficient, external-memory, and write-efficient algorithms for fundamental problems in computing. Many of the principles of algorithm engineering will be illustrated in the context of parallel algorithms and graph problems.
This course treats various methods to design and analyze datastructures and algorithms for a wide range of problems. The most important new datastructure treated is the graph, and the general methods introduced are: greedy algorithms, divide and conquer, dynamic programming and network flow algorithms. These general methods are explained by a number of concrete examples, such as simple scheduling algorithms, Dijkstra, Ford-Fulkerson, minimum spanning tree, closest-pair-of-points, knapsack, and Bellman-Ford. Throughout this course there is significant attention to proving the correctness of the discussed algorithms. All material for this course is in English. The recorded lectures, however, are in Dutch.