Effective Philanthropy: Towards a Research Agenda - A White Paper

Many people look at getting people to give more. Giving Evidence and the Social Enterprise Initiative at the University of Chicago Booth School of Business have been looking at getting donors to give better. Perhaps improving giving will achieve more than increasing it: For instance, the cost of raising capital for charities is about 20-40 per cent, against only about 3-5 per cent for companies, and charities turn away some donors who are fiddly to deal with. It may be easier to reduce that cost of capital than to raise the amount given. Plus, money doesn't always go where it's most needed: for example, about 90 per cent of global health spending goes on 10 percent of the disease burden -- maybe those donations can cheaply be re-directed. Our white paper looks at at (i)what good giving is, i.e., what donor behaviours produce the best outcomes, and (ii)how to persuade/enable/nudge donors to do those behaviours. It collates what is known on these topics, and lays out many unanswered questions which would form a strong research agenda. [The Chicago Booth School of Business was recently ranked by The Economist as the best business school in the world. And its leading centre on decision science is highly relevant since decisions are so integral to giving.] The white paper identifies questions which non-profits, funders and other practitioners want answered about making giving better, and aims to encourage researchers to address them

Proceedings of the COST SUSVAR/ECO-PB Workshop on organic plant breeding strategies and the use of molecular markers

In many countries,national projects are in progress to investigate the sustainable low-input approach.In the present COST network,these projects are coordinated by means of exchange of materials,establishing common methods for assessment and statistical analyses and by combining national experimental results.The common framework is cereal production in low-input sustainable systems with emphasis on crop diversity.The network is organised into six Working Groups,five focusing on specific research areas and one focusing on the practical application of the research results for variety testing:1)plant genetics and plant breeding,2)biostatistics,3)plant nutrition and soil microbiology,4)weed biology and plant competition,5)plant pathology and plant disease resistance biology and 6)variety testing and certification.It is essential that scientists from many disciplines work together to investigate the complex interactions between the crop and its environment,in order to be able to exploit the natural regulatory mechanisms of different agricultural systems for stabilising and increasing yield and quality.The results of this cooperation will contribute to commercial plant breeding as well as official variety testing,when participants from these areas disperse the knowledge achieved through the EU COST Action

Ethics and taxation : a cross-national comparison of UK and Turkish firms

This paper investigates responses to tax related ethical issues facing busines

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

Multimodal Biometric Systems for Personal Identification and Authentication using Machine and Deep Learning Classifiers

Multimodal biometrics, using machine and deep learning, has recently gained interest over single biometric modalities. This interest stems from the fact that this technique improves recognition and, thus, provides more security. In fact, by combining the abilities of single biometrics, the fusion of two or more biometric modalities creates a robust recognition system that is resistant to the flaws of individual modalities. However, the excellent recognition of multimodal systems depends on multiple factors, such as the fusion scheme, fusion technique, feature extraction techniques, and classification method. In machine learning, existing works generally use different algorithms for feature extraction of modalities, which makes the system more complex. On the other hand, deep learning, with its ability to extract features automatically, has made recognition more efficient and accurate. Studies deploying deep learning algorithms in multimodal biometric systems tried to find a good compromise between the false acceptance and the false rejection rates (FAR and FRR) to choose the threshold in the matching step. This manual choice is not optimal and depends on the expertise of the solution designer, hence the need to automatize this step. From this perspective, the second part of this thesis details an end-to-end CNN algorithm with an automatic matching mechanism. This thesis has conducted two studies on face and iris multimodal biometric recognition. The first study proposes a new feature extraction technique for biometric systems based on machine learning. The iris and facial features extraction is performed using the Discrete Wavelet Transform (DWT) combined with the Singular Value Decomposition (SVD). Merging the relevant characteristics of the two modalities is used to create a pattern for an individual in the dataset. The experimental results show the robustness of our proposed technique and the efficiency when using the same feature extraction technique for both modalities. The proposed method outperformed the state-of-the-art and gave an accuracy of 98.90%. The second study proposes a deep learning approach using DensNet121 and FaceNet for iris and faces multimodal recognition using feature-level fusion and a new automatic matching technique. The proposed automatic matching approach does not use the threshold to ensure a better compromise between performance and FAR and FRR errors. However, it uses a trained multilayer perceptron (MLP) model that allows people’s automatic classification into two classes: recognized and unrecognized. This platform ensures an accurate and fully automatic process of multimodal recognition. The results obtained by the DenseNet121-FaceNet model by adopting feature-level fusion and automatic matching are very satisfactory. The proposed deep learning models give 99.78% of accuracy, and 99.56% of precision, with 0.22% of FRR and without FAR errors. The proposed and developed platform solutions in this thesis were tested and vali- dated in two different case studies, the central pharmacy of Al-Asria Eye Clinic in Dubai and the Abu Dhabi Police General Headquarters (Police GHQ). The solution allows fast identification of the persons authorized to access the different rooms. It thus protects the pharmacy against any medication abuse and the red zone in the military zone against the unauthorized use of weapons

Evaluating Evaluations of Clinical Decision Support Systems: Case Studies From NHS Clinical Settings

The NHS is under increasing pressure to cut costs while delivering high quality care. At the same time, the demand for healthcare services has grown, driven in part by the increasing number of older people in the population. NHS Trusts are adopting clinical decision support systems (CDSSs) to help decision making at the point of care. CDSSs are said to bring benefits such as improvements in guideline adherence, clinical processes and user performance but evidence of these benefits is not always available and their effectiveness in terms of improving patient outcomes is often open to question. This thesis presents research that was carried out in a large teaching NHS Trust looking at the evaluations of three CDSSs. Semi structured interviews were carried out with key informants who were involved in their adoption, use and evaluations. Documentary analysis and observations were also used to augment the interviews. Most evaluations were carried out informally by the developers and were primarily driven by external regulatory pressures rather than patient outcomes and organisational needs. Evaluation documentation was inadequate or missing, thus making it difficult to systematically assess these evaluations. This thesis contends that evaluations are important to provide decision makers in NHS Trusts with adequate information to make decisions about CDSSs and computerised healthcare information technologies in general. NHS Trusts need to build organisational capacity and readiness to enable them to effectively carry out evaluations that will provide meaningful information to gain better understanding of CDSSs and to inform their successful adoption, implementation, usage and to justify the resource allocation. This research shows that CDSS evaluations investigated took a predominantly narrow view. It thus provides evidence for the need for a more systemic approach to evaluation