Artificial Intelligence Model for Flowable Concrete Mixtures Used in Underwater Construction and Repair

This study explores using artificial neural networks to predict the rheological and mechanical properties of underwater concrete (UWC) mixtures and to evaluate the sensitivity of such properties to variations in mixture ingredients. Artificial neural networks (ANN) mimic the structure and operation of biological neurons and have the unique ability of self-learning, mapping, and functional approximation. Details of the development of the proposed neural network model, its architecture, training, and validation are presented in this study. A database incorporating 175 UWC mixtures from nine different studies was developed to train and test the ANN model. The data are arranged in a patterned format. Each pattern contains an input vector that includes quantity values of the mixture variables influencing the behavior of UWC mixtures (that is, cement, silica fume, fly ash, slag, water, coarse and fine aggregates, and chemical admixtures) and a corresponding output vector that includes the rheological or mechanical property to be modeled. Results show that the ANN model thus developed is not only capable of accurately predicting the slump, slump-flow, washout resistance, and compressive strength of underwater concrete mixtures used in the training process, but it can also effectively predict the aforementioned properties for new mixtures designed within the practical range of the input parameters used in the training process with an absolute error of 4.6, 10.6, 10.6, and 4.4%, respectively.

Introduction to Domain-Driven Data Mining Special Section

In the last decade, data mining has emerged as one of the most dynamic and lively areas in information technology. Although many algorithms and techniques for data mining have been proposed, they either focus on domain independent techniques or on very specific domain problems. A general requirement in bridging the gap between academia and business is to cater to general domain-related issues surrounding real-life applications, such as constraints, organizational factors, domain expert knowledge, domain adaption, and operational knowledge. Unfortunately, these either have not been addressed, or have not been sufficiently addressed, in current data mining research and development.Domain-Driven Data Mining (D3M) aims to develop general principles, methodologies, and techniques for modeling and merging comprehensive domain-related factors and synthesized ubiquitous intelligence surrounding problem domains with the data mining process, and discovering knowledge to support business decision-making. This paper aims to report original, cutting-edge, and state-of-the-art progress in D3M. It covers theoretical and applied contributions aiming to: 1) propose next-generation data mining frameworks and processes for actionable knowledge discovery, 2) investigate effective (automated, human and machine-centered and/or human-machined-co-operated) principles and approaches for acquiring, representing, modelling, and engaging ubiquitous intelligence in real-world data mining, and 3) develop workable and operational systems balancing technical significance and applications concerns, and converting and delivering actionable knowledge into operational applications rules to seamlessly engage application processes and systems.

Effect of genotype on changes in intelligence quotient after dietary relaxation in phenylketonuria and hyperphenylalaninaemia

Background-Associations between genotype and intellectual outcome in patients with phenylketonuria are complicated because intelligence is influenced by many variables, including environmental factors and other genetic determinants. Intellectual changes with age, both on and after relaxation of diet, vary within the patient population. This study aims to determine whether a significant association exists between genotype and change in intelligence after relaxation of diet.

The psychological effects of organizational restructuring on nurses

This paper reports a comparison of nurses affected by the restructuring associated with healthcare organization mergers (1998-2000) in the United Kingdom and those of non-affected nurses in the UK.

Transaction Costs and Organizational Learning in Strategic Oursourcing Relationships

The present paper examines the role of organisational learning and transaction costs economics in strategic outsourcing decisions. Interorganisational learning is critical to competitive success, and organisations often learn more effectively by collaborating with other organisations. However, learning processes may also complicate the process of forming interorganisational partnerships which may increase transaction costs. Based on the literature, the authors develop refutable implications for outsourcing supply chain logistics and a sample of 121 firms in the supply chain logistics industry is used to test the hypotheses. The results show that trust and transaction costs are significant and substantial drivers of strategic outsourcing of supply chain logistics (a strategic flexibility action). Learning intent and knowledge acquisition have no significant influence on the decision to outsource supply chain logistics. The paper concludes with a discussion of the different and often conflicting implications for managing interorganisational learning processes.

An exploration of the importanceof emotional intelligence in midwifery

Background, Recognition of the importance of Emotional intelligence dates back as far as Aristotle (350BC). More recently the notion of emotional intelligence features in social psychology literature; it has also been embraced within personnel management and is now beginning to appear in nursing, medical and midwifery journals. Emotional intelligence involves possessing the capacity for motivation, creativity, the ability to operate at peak performance and the ability to persist in the face of setbacks and failures. Emotional intelligence refers to the ability to recognise our own feelings and those of others and it enables us to manage emotions effectively in ourselves and in our relationships. Midwives are constantly responding to change and challenges within maternity services. This paper examines how emotional intelligence can assist midwives in dealing with pressures which involve delivering the Government reforms, providing choice to women and facing current issues within the midwifery workforce. Midwives need emotional intelligence in order to express their feelings and recognize the feelings of others. Enhancing our relationships with colleagues and clients will ultimately impact on the quality of care delivered to women. Overall the aims of the paper are to create an awareness of the importance of emotional intelligence in practice and define emotional intelligence.