There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

MKT 6009 Marketing Internship (0 semester credit hours) Student gains experience and improves skills through appropriate developmental work assignments in a real business environment. Student must identify and submit specific business learning objectives at the beginning of the semester. The student must demonstrate exposure to the managerial perspective via involvement or observation. At semester end, student prepares an oral or poster presentation, or a written paper reflecting on the work experience. Student performance is evaluated by the work supervisor. Pass/Fail only. Prerequisites: (MAS 6102 or MBA major) and department consent required. (0-0) S MKT 6244 Digital Marketing Strategy (2 semester credit hours) Executive Education Course. The course explores three distinct areas within marketing and sales namely, digital marketing, traditional sales prospecting, and executive sales organization and strategy. The continuing convergence of the digital marketing and sales funnels has created a strategic continuum from digital lead generation to digital sales. The course identifies the current composition of this digital continuum while providing opportunities to evaluate sales and marketing digital strategies. Prerequisites: MKT 6301 and instructor consent required. (2-0) Y MKT 6301 (SYSM 6318) Marketing Management (3 semester credit hours) Overview of marketing management methods, principles and concepts including product, pricing, promotion and distribution decisions as well as segmentation, targeting and positioning. (3-0) S MKT 6309 Marketing Data Analysis and Research (3 semester credit hours) Methods employed in market research and data analysis to understand consumer behavior, customer journeys, and markets so as to enable better decision-making. Topics include understanding different sources of data, survey design, experiments, and sampling plans. The course will cover the techniques used for market sizing estimation and forecasting. In addition, the course will cover the foundational concepts and techniques used in data visualization and \"story-telling\" for clients and management. Corequisites: MKT 6301 and OPRE 6301. (3-0) Y MKT 6310 Consumer Behavior (3 semester credit hours) An exposition of the theoretical perspectives of consumer behavior along with practical marketing implication. Study of psychological, sociological and behavioral findings and frameworks with reference to consumer decision-making. Topics will include the consumer decision-making model, individual determinants of consumer behavior and environmental influences on consumer behavior and their impact on marketing. Prerequisite: MKT 6301. (3-0) Y MKT 6321 Interactive and Digital Marketing (3 semester credit hours) Introduction to the theory and practice of interactive and digital marketing. Topics covered include: online-market research, consumer behavior, conversion metrics, and segmentation considerations; ecommerce, search and display advertising, audiences, search engine marketing, email, mobile, video, social networks, and the Internet of Things. (3-0) T MKT 6322 Internet Business Models (3 semester credit hours) Topics to be covered are: consumer behavior on the Internet, advertising on the Internet, competitive strategies, market research using the Internet, brand management, managing distribution and supply chains, pricing strategies, electronic payment systems, and developing virtual organizations. Further, students learn auction theory, web content design, and clickstream analysis. Prerequisite: MKT 6301. (3-0) Y MKT 6323 Database Marketing (3 semester credit hours) Techniques to analyze, interpret, and utilize marketing databases of customers to identify a firm's best customers, understanding their needs, and targeting communications and promotions to retain such customers. Topics

Marketing for management.

行銷硏究 : Marketing research

This handbook looks to provide academics and students with a comprehensive and holistic understanding of the phenomenon of innovation. Innovation spans a number of fields within the social sciences and humanities: Management, Economics, Geography, Sociology, Politics, Psychology, and History. Consequently, the rapidly increasing body of literature on innovation is characterized by a multitude of perspectives based on, or cutting across, existing disciplines and specializations. Scholars of innovation can come from such diverse starting points that much of this literature can be missed, and so constructive dialogues missed. The editors of The Oxford Handbook of Innovation have carefully selected and designed twenty-one contributions from leading academic experts within their particular field, each focusing on a specific aspect of innovation. These have been organized into four main sections, the first of which looks at the creation of innovations, with particular focus on firms and networks. Section Two provides an account of the wider systematic setting influencing innovation and the role of institutions and organizations in this context. Section Three explores some of the diversity in the working of innovation over time and across different sectors of the economy, and Section Four focuses on the consequences of innovation with respect to economic growth, international competitiveness, and employment. An introductory overview, concluding remarks, and guide to further reading for each chapter, make this handbook a key introduction and vital reference work for researchers, academics, and advanced students of innovation. Contributors to this volume - Jan Fagerberg, University of Oslo William Lazonick, INSEAD Walter W. Powell, Stanford University Keith Pavitt, SPRU Alice Lam, Brunel University Keith Smith, INTECH Charles Edquist, Linkoping David Mowery, University of California, Berkeley Mary O'Sullivan, INSEAD Ove Granstrand, Chalmers Bjorn Asheim, University of Lund Rajneesh Narula, Copenhagen Business School Antonello Zanfei, Urbino Kristine Bruland, University of Oslo Franco Malerba, University of Bocconi Nick Von Tunzelmann, SPRU Ian Miles, University of Manchester Bronwyn Hall, University of California, Berkeley Bart Verspagen , ECIS Francisco Louca, ISEG Manuel M. Godinho, ISEG Richard R. Nelson, Mario Pianta, Urbino Bengt-Ake Lundvall, Aalborg

The Oxford Handbook of Innovation

The implementation of Industry 4.0 technologies has improved the flexibility of the entire manufacturing system. These technologies are the Internet of Things (IoT), big data, Artificial Intelligence (AI), Additive Manufacturing (AM), advanced robotics, virtual reality, cloud computing, simulation, and among others, have arisen to improve the flexibility in the entire manufacturing system. Industry 4.0 is becoming recognised as a unique industrial paradigm. It is predicated on the widespread adoption of communication and information technology, which would lead to improved organisational performance and flexibility. The incorporation of Industry 4.0 is the true game-changer in terms of flexibility and customisation. Manufacturers may utilise this technology to build digital twins of items used by consumers in the real world. The digital twin gets real-time information from sensors on the actual objects. Manufacturers benefit from digital twin and simulation technology, including predictive maintenance and making errors easier and faster to rectify. This paper discusses Industry 4.0 and its Flexible Manufacturing System (FMS) capability. Different dimensions and technologies of Industry 4.0 Practices for improving FMS performance are studied,d and then discusses several flexible approaches using Industry 4.0 technologies are. One of the most significant benefits of adopting virtual infrastructure maintained by a service provider is improved flexibility. Cloud services allow auto-scaling, which means that the underlying computer resources automatically adjust to changing utilisation rates. Industry 4.0 increases production flexibility, allowing a facility to respond to market changes quickly. A plant control system automatically varies output depending on shifting utility rates, lowering production costs. Industry 4.0 offers some incredible benefits and has gone a long way in the last several years. 

Enabling flexible manufacturing system (FMS) through the applications of industry 4.0 technologies

The Fourth Industrial Revolution (Industry 4.0) has revolutionised the operations, technologies, logistics and supply chain. To align with Industry 4.0, the conventional logistics processes need to upgrade into the digital environment. This transformation of conventional logistics into digital logistics (logistics 4.0) is possible through exploring several critical success factors that are responsible for the adoption of logistics 4.0. Therefore, this study analyses the critical success factors of logistics 4.0. In order to do this, a comprehensive literature review is conducted for the identification of the critical success factors of logistics 4.0. After that, a focus group discussion with the experts is conducted and ten critical success factors are finalised. These critical success factors are interrelated, and this interrelationship is explored through the DEMATEL approach. This method also categorised the critical success factors into cause-and-effect groups. Six factors belong to the cause group, and the remaining four belong to the effect group. This analysis also recognised the most influential and influenced factor amongst all identified critical success factors that will help the practitioners to optimal utilisation of the resources. The ‘top management commitment and support’, ‘aligning the initiatives of logistic 4.0 with organizational strategy’ and ‘technological infrastructure’ are the most influential critical success factors. The finding of the study is expected to support the scholars and practitioners by putting their effort strategically on the influencing critical success factors. This study will also facilitate the managers that are willing to move towards logistics 4.0 in the Industry 4.0 environment.

/pdf/exploration-of-critical-success-factors-of-logistics-4-0-a-la2jkpf9.pdf

Exploration of Critical Success Factors of Logistics 4.0: A DEMATEL Approach

Purpose




Need for effective adoption of halal certification through assessment and accreditation (HCAA) is imperative for the higher level of customer satisfaction. To achieve this, all stakeholders need to be involved in developing the policy. Thus, this study aims to identify barriers to the adoption of HCAA and analyses through structural model of interrelated barriers




Design/methodology/approach




The structural and hierarchical model of barriers to the adoption of HCAA is developed after extensive systematic literature survey along with opinions from various types of experts. Interpretive structural modelling is identified as the appropriate tool in making this model, which is further analysed using MICMAC (Matriced’ Impacts croises-multipication applique’ and classment). Corresponding issues for every barrier as identified may help in further developing the action plan for each stakeholder. Objectives and action plan for various stakeholders were evolved and provided.




Findings




The significant finding indicates to developing a globally accepted halal certifying organisation, as to contain the mislabelling, and this further needs extensive government and customer support. The customer needs to be more aware of the proper idea of halal. Therefore, to succeed, the industry needs to develop a brand identity with a distinct/unique/clear marketing message, not just certifying products/services as halal.




Originality/value




Specific direction for different stakeholders has been derived along with academic finding for researchers and to further develop the action plan.

Halal certification, the inadequacy of its adoption, modelling and strategising the efforts

A workshop on green construction was organized in New Delhi in July 2008 by Jamia Millia University, India and University of Salford, U.K., aiming to increase 'green' practices that are environmentally friendly and energy efficient. The workshop included participants from regulatory bodies, public and private construction companies and researchers. The object of the workshop was to provide an opportunity to capture issues, challenges and research issues in green practices throughout the supply chain in the construction sector in India. The methodology constituted a survey that was provided to all participants, where eight questions were composed by the facilitators. The participants were asked about their views on voluntary and compulsory ways of assessment and audit of green implementation in India. The findings of the workshop featured major challenges, drivers, initiatives, and the ways of effective implementation as well as enforcement on the discussed topic.

/pdf/green-construction-in-india-gaining-a-deeper-understanding-3vios0s0m6.pdf

Abid Haleem

Papers

Enabling flexible manufacturing system (FMS) through the applications of industry 4.0 technologies

Exploration of Critical Success Factors of Logistics 4.0: A DEMATEL Approach

Halal certification, the inadequacy of its adoption, modelling and strategising the efforts

Green construction in India: Gaining a deeper understanding

Three-Dimensional-Printed Polyether Ether Ketone Implants for Orthopedics.