Courses with Gaps

Fundamental structural components of the railway track and how they are designed to withstand the effects of train and environmental forces so that the railway foundation is adequately protected and train / track operating costs, passenger comfort and safety are kept within acceptable limits. Track structure as a whole and its components parts including the rails, fastenings, sleepers, ballast, the formation and subsoil. Switches and crossings and the overhead line electrification system. Aspects of concreted (slab track). Track maintenance.

Introduction to Euro codes for design of building structures, design principles for reinforced concrete structures covering beams, columns, slabs, footings and retaining walls. Slabs: types of slabs, design methods and procedures, One-way spanning solid slabs, One-way spanning ribbed slabs, Two-way spanning solid slabs, restrained solid slabs, Waffle slabs, flat slabs. Columns: Types, loads, classification and design considerations, effective heights of columns, short braced axially loaded columns, short columns subjected to axial load and bending about one axis, symmetrical and unsymmetrical reinforcement, column sections subjected to axial and biaxial bending, design of slender columns. Foundations: Functions, types and general considerations, isolated pad bases/eccentrically loaded pad bases. Wall, strip and combined foundations. Software for reinforced concrete design of building structures. Design One-storey live project. Introduction to deep beam and corbel.

Current and potential future energy systems in Nigeria and globally - resources, extraction, concepts in energy conversion systems; parallels and differences in various conversion systems and end-use technologies, with emphasis on meeting 21st-century national, regional and global energy needs in a sustainable manner. Various energy technologies in each fuel cycle stage for fossil (oil, gas, synthetic), nuclear (fission and fusion) and renewable (solar, biomass, wind, hydro, and geothermal). Energy types, storage, transmission and conservation. Analysis of energy mixes within an engineering, economic and social context. Sustainable energy; emphasise sustainability in general and in the overall concept of sustainable development and the link this has with sustainable energy as the fundamental benefit of renewable energy. Practical Content: Simple measurement of solar radiation, bomb calorimeter determination of calorific value of fuels and biomass; measurement of the velocity of wind, waves and the energy that abound in them; laboratory production of biogas and determination of energy available in it; simple conversion of solar energy to electricity; transesterification of edible oil into biodiesel; simulation of geothermal energy; Geiger-Muller or Scintillation Counters’ determination of uranium or thorium energy; simple solid or salt storage of energy; hybrid application of renewable energy. GET 307: Introduction to Artificial Intelligence, Machine Learning and Convergent Technologies (3 Units C: LH 45) Learning Outcomes At the completion of the course, the students are expected to be able: 1. explain the meaning, purpose, scope, stages, applications and effects of artificial intelligence; 2. explain the fundamental concepts of machine learning, deep learning and convergent technologies; 3. demonstrate the difference between supervised, semi-supervised and unsupervised learning; 4. demonstrate proficiency in machine learning workflow and how to implement the steps effectively; 5. explain natural languages, knowledge representation, expert systems and pattern recognition; 6. describe distributed systems, data and information security and intelligent web technologies; 7. explain the concept of big data analytics, purpose of studying it, issues that can arise with a data set and the importance of properly preparing data prior to a machine learning exercise; and 8. explain the concepts, characteristics, models and benefits, key security and compliance challenges of cloud computing. Course Contents Concepts of human and artificial intelligence; artificial/computational intelligence paradigms; search, logic and learning algorithms. Machine learning and nature-inspired algorithms – examples, their variants and applications to solving engineering problems; understanding natural languages; knowledge representation, knowledge elicitation, mathematical and logic foundations of AI; expert systems, automated reasoning and pattern recognition; distributed systems; data and information security; intelligent web technologies; convergent technologies – definition, significance and engineering applications. Neural networks and deep learning. Introduction to python AI libraries.

Current and potential future energy systems in Nigeria and globally - resources, extraction, concepts in energy conversion systems; parallels and differences in various conversion systems and end-use technologies, with emphasis on meeting 21st-century national, regional and global energy needs in a sustainable manner. Various energy technologies in each fuel cycle stage for fossil (oil, gas, synthetic), nuclear (fission and fusion) and renewable (solar, biomass, wind, hydro, and geothermal). Energy types, storage, transmission and conservation. Analysis of energy mixes within an engineering, economic and social context. Sustainable energy; emphasise sustainability in general and in the overall concept of sustainable development and the link this has with sustainable energy as the fundamental benefit of renewable energy. Practical Content: Simple measurement of solar radiation, bomb calorimeter determination of calorific value of fuels and biomass; measurement of the velocity of wind, waves and the energy that abound in them; laboratory production of biogas and determination of energy available in it; simple conversion of solar energy to electricity; transesterification of edible oil into biodiesel; simulation of geothermal energy; Geiger-Muller or Scintillation Counters’ determination of uranium or thorium energy; simple solid or salt storage of energy; hybrid application of renewable energy. GET 307: Introduction to Artificial Intelligence, Machine Learning and Convergent Technologies (3 Units C: LH 45) Learning Outcomes At the completion of the course, the students are expected to be able: 1. explain the meaning, purpose, scope, stages, applications and effects of artificial intelligence; 2. explain the fundamental concepts of machine learning, deep learning and convergent technologies; 3. demonstrate the difference between supervised, semi-supervised and unsupervised learning; 4. demonstrate proficiency in machine learning workflow and how to implement the steps effectively; 5. explain natural languages, knowledge representation, expert systems and pattern recognition; 6. describe distributed systems, data and information security and intelligent web technologies; 7. explain the concept of big data analytics, purpose of studying it, issues that can arise with a data set and the importance of properly preparing data prior to a machine learning exercise; and 8. explain the concepts, characteristics, models and benefits, key security and compliance challenges of cloud computing. Course Contents Concepts of human and artificial intelligence; artificial/computational intelligence paradigms; search, logic and learning algorithms. Machine learning and nature-inspired algorithms – examples, their variants and applications to solving engineering problems; understanding natural languages; knowledge representation, knowledge elicitation, mathematical and logic foundations of AI; expert systems, automated reasoning and pattern recognition; distributed systems; data and information security; intelligent web technologies; convergent technologies – definition, significance and engineering applications. Neural networks and deep learning. Introduction to python AI libraries.

Current and potential future energy systems in Nigeria and globally - resources, extraction, concepts in energy conversion systems; parallels and differences in various conversion systems and end-use technologies, with emphasis on meeting 21st-century national, regional and global energy needs in a sustainable manner. Various energy technologies in each fuel cycle stage for fossil (oil, gas, synthetic), nuclear (fission and fusion) and renewable (solar, biomass, wind, hydro, and geothermal). Energy types, storage, transmission and conservation. Analysis of energy mixes within an engineering, economic and social context. Sustainable energy; emphasise sustainability in general and in the overall concept of sustainable development and the link this has with sustainable energy as the fundamental benefit of renewable energy. Practical Contents Simple measurement of solar radiation, bomb calorimeter determination of calorific value of fuels and biomass; measurement of the velocity of wind, waves and the energy that abound in them; laboratory production of biogas and determination of energy available in it; simple conversion of solar energy to electricity; transesterification of edible oil into biodiesel; simulation of geothermal energy; Geiger-Muller or Scintillation Counters’ determination of uranium or thorium energy; simple solid or salt storage of energy; hybrid application of renewable energy. GET 307: Introduction to Artificial Intelligence, Machine Learning and Convergent Technologies (3 Units C: LH 45) Learning Outcomes At the completion of the course, the students are expected to be able: 1. explain the meaning, purpose, scope, stages, applications and effects of artificial intelligence; 2. explain the fundamental concepts of machine learning, deep learning and convergent technologies; 3. demonstrate the difference between supervised, semi-supervised and unsupervised learning; 4. demonstrate proficiency in machine learning workflow and how to implement the steps effectively; 5. explain natural languages, knowledge representation, expert systems and pattern recognition; 6. describe distributed systems, data and information security and intelligent web technologies; 7. explain the concept of big data analytics, purpose of studying it, issues that can arise with a data set and the importance of properly preparing data prior to a machine learning exercise; and 8. explain the concepts, characteristics, models and benefits, key security and compliance challenges of cloud computing. Course Contents Concepts of human and artificial intelligence; artificial/computational intelligence paradigms; search, logic and learning algorithms. Machine learning and nature-inspired algorithms – examples, their variants and applications to solving engineering problems; understanding natural languages; knowledge representation, knowledge elicitation, mathematical and logic foundations of AI.; expert systems, automated reasoning and pattern recognition; distributed systems; data and information security; intelligent web technologies; convergent technologies – definition, significance and engineering applications. Neural networks and deep learning. Introduction to python “AI” libraries.

Review of methodologies in Surveying and Geoinformatics. Techniques in research methods. Identification of research problems, review of related literature and justification for the research. Description of data source, acquisition, quality and presentation of data, processing of data. Presentation and analysis of results, research findings, conclusions, recommendations and research contributions to knowledge. Rreferences and appendices.

Review of the anatomy of digestive system of the ruminant animals; Patho-physiology of the ruminant digestive tract; traumatic reticulitis; tympani and torsion; Nutrition and metabolism of ruminants

Concept of integrated rural development (planning and implementation). Overview of the problems of rural infrastructures. Review of agricultural construction survey. Rural road network. Rural road design, construction and maintenance; erosion of earth roads; minor road crossing. Small scale irrigation; rural electricity; rural water supplies; rural sanitation. Practical contents: A levelling survey exercise for road construction. Excursion: Visit to an earth dam site and an irrigation project. 400 level GET 402 ENGINEERING Project I (2 Units: C; PH 90) Learning Outcomes At the end of this course, the students should be able to: 1. Complete the design phase of a complex engineering problem sourced from industry or community during the SIWES III programme. 2. Demonstrate the connection between engineering product-making and the theoretical courses they have learned following the applicable industry best practices. Course Contents In the second semester of the 400-level students, preferably in groups, work from the university on the identified industry or organization to tackle industry complex engineering problems. Theoretical issues may be provided by the department faculty or industry experts. During the vacation, students will now work full time with the organisation/industry on the project as part of the SIWES III. The students can also go beyond the department and engage in multidisciplinary undertakings. Literature survey, review of existing systems etc. must be achieved to a satisfactory extent. GET 404 Engineering Valuation and Appraisal (2 Units: C; LH 30) Learning Outcomes At the end of this course, the students should be able to: 1. Identify at least three (3) objectives of engineering valuation work, valuer's primary duty and responsibility and valuation terminologies. 2. Describe at least four (4) Valuer's obligation to his or her client, to other valuers, and to the society. 3. Demonstrate with example the engineering valuation methods, valuation standards, and practices. 4. Prepare engineering valuation and appraisal reports and review 5. Discuss expert witnessing and ethics in valuation. 6. Determine price, cost, value, depreciation and obsolescence in real property, personal property, personal property, machinery and equipment, oil, gas, mines, and quarries valuation. Course Content Objectives of valuation work/ valuer's primary duty and responsibility. Valuer's obligation to his or her client, to other valuers, and to the society. Valuation methods and practices. Valuation reports. Expert witnessing. Ethics in valuation. Valuation standards. Price, cost and value. Depreciation and obsolescence. Valuation terminology. Real asset valuation; personal asset valuation. Machinery and equipment valuation. Oil and gas facilities valuation. Mines and quarries valuation. Appraisal reporting and review. ABE 401: Instrumentation and Measurement in Agricultural and Biosystems Engineering (2 Units C: LH 15; PH 45) Learning Outcomes This course will help students to: 1. identify the appropriate instruments for measuring parametres relevant to agricultural activities; 2. manage the acquisition, transmission, recording, analysing and computing of data; and 3. apply these instruments, particularly for research in agricultural and biosystems engineering. Course Contents Motion, force, torque and shaft power, pressure and sound flux; humidity measurement; application of primary sensing element; data manipulation, computing and compensating devices; data transmission and recording.