A classification of predictive-reactive project scheduling procedures.

The vast majority of the project scheduling research efforts over the past several years have concentrated on the development of workable predictive baseline schedules, assuming complete information and a static and deterministic environment. During execution, however, a project may be subject to numerous schedule disruptions. Proactive-reactive project scheduling procedures try to cope with these disruptions through the combination of a proactive scheduling procedure for generating predictive baseline schedules that are hopefully robust in that they incorporate safety time to absorb anticipated disruptions with a reactive procedure that is invoked when a schedule breakage occurs during project execution.proactive-reactive project scheduling; time uncertainty; stability; timely project completion; preselective strategies; resource constraints; trade-off; complexity; stability; management; makespan; networks; subject; job;

Active local distribution network management for embedded generation

Traditionally, distribution networks have been operated as passive networks with uni-directional power flows. With the connection of increasing amounts of distributed generation, these networks are becoming active with power flowing in two directions, hence requiring more intelligent forms of management. The report into issues for access to electricity networks published by the Ofgem/DTI Embedded Generation Working Group in January 2001 called for new work in the area of active distribution network management. The report suggested an evolution from the present passive network control philosophy to fully active network control methods. In line with these recommendations Econnect is developing a new type of distribution network controller, called GenAVC. GenAVC is a controller for electricity distribution networks that aims to increase the amount of energy that can be exported onto the distribution networks by generating plants. The UK is leading the world in electricity de-regulation and one aspect of this is the increasing demand for the connection of distributed generation. Active distribution network management is seen to be essential for networks to accommodate the levels of distributed generation that are predicted for 2010. The work being undertaken as part of this project is therefore at the forefront of international network management technology

Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)

In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a word given previous words and an image. Image captions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on four benchmark datasets (IAPR TC-12, Flickr 8K, Flickr 30K and MS COCO). Our model outperforms the state-of-the-art methods. In addition, we apply the m-RNN model to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval. The project page of this work is: www.stat.ucla.edu/~junhua.mao/m-RNN.html .Comment: Add a simple strategy to boost the performance of image captioning task significantly. More details are shown in Section 8 of the paper. The code and related data are available at https://github.com/mjhucla/mRNN-CR ;. arXiv admin note: substantial text overlap with arXiv:1410.109

Autonomous deployment and repair of a sensor network using an unmanned aerial vehicle

We describe a sensor network deployment method using autonomous flying robots. Such networks are suitable for tasks such as large-scale environmental monitoring or for command and control in emergency situations. We describe in detail the algorithms used for deployment and for measuring network connectivity and provide experimental data we collected from field trials. A particular focus is on determining gaps in connectivity of the deployed network and generating a plan for a second, repair, pass to complete the connectivity. This project is the result of a collaboration between three robotics labs (CSIRO, USC, and Dartmouth.)

IDEAS project - Professional advice network study in Ethiopia

The IDEAS project sought to improve the health and survival of mothers and babies through generating evidence to inform policy and practice. This collection contains quantitative data, collection tools and documentation associated with a social network analysis study of the professional advice networks used by health care workers in Ethiopia. The cross-sectional, mixed-methods observational network study compared professional advice networks of 160 healthcare workers in 8 primary health care units (PHCUs) across four regions of Ethiopia; Amhara, Oromia, SNNP and Tigray. PHCUs include a health centre and typically 5 satellite health posts. Data captured included health care worker advice seeking and giving for the provision of four areas along the continuum of maternal and newborn care: antenatal care, childbirth care, postnatal care and newborn care. Additional information captured regarded professional advice exchange beyond the roster of health care workers in the PHCU. Network metrics were qualitatively compared to continuum of care coverage data as a secondary analysis. Twenty semi-structured qualitative interviews of purposively selected subjects followed the collection of quantitative network data to interpret and explain network roles and patterns observed

Paving the Road to Better Income Options: Case Study on Promoting Women’s Livelihood and Employment Opportunities in Viet Nam

[Excerpt] Infrastructure projects and road networks have he potential to catalyze socioeconomic change. hey yield economic prospects for neighboring communities, enhance higher-value agricultural and industrial investment, and improve access to markets as well as health and social services. Unfortunately, such projects also entail risks. Residents are often excluded from the benefits either because they lack awareness of the opportunities or they lack the skills that could make them direct beneficiaries. Additionally, as is frequently observed, residents can become vulnerable to unforeseen risks, such as human trafficking and the spread of HIV, which can result from the increased physical connectivity. Bearing in mind both the rewards and risks of large-scale infrastructure projects, the 16-month project “Promoting Gender Equality and Women’s Empowerment— Strengthening Capacity of Women Along the Central Mekong Delta Connectivity Project Phase II” was designed to prepare local communities for both income opportunities and potential adverse impacts with the forthcoming road construction in Dong Thap Province, in southern Viet Nam. With a total budget of $400,000, the project began in June 2012 targeting women, both those who are married and younger single women who might out-migrate for employment. The project set out to improve income-generating opportunities for women, promote gender equality, and enhance awareness of the risks related to human trafficking and HIV in eight communes located in two districts (Cao Lanh and Thap Muoi). Specifically, women from low-income households were targeted with vocational skills training and access to credit and employment placements. Simultaneously, the project emphasized building up institutional capacities for improving livelihood opportunities for women that would endure beyond the project

HENNOVATION: Learnings from Promoting Practice-Led Multi-Actor Innovation Networks to Address Complex Animal Welfare Challenges within the Laying Hen Industry

The Hennovation project, an EU H2020 funded thematic network, aimed to explore the potential value of practice-led multi-actor innovation networks within the laying hen industry. The project proposed that husbandry solutions can be practice-led and effectively supported to achieve durable gains in sustainability and animal welfare. It encouraged a move away from the traditional model of science providing solutions for practice, towards a collaborative approach where expertise from science and practice were equally valued. During the 32-month project, the team facilitated 19 multi-actor networks in five countries through six critical steps in the innovation process: problem identification, generation of ideas, planning, small scale trials, implementation and sharing with others. The networks included farmers, processors, veterinarians, technical advisors, market representatives and scientists. The interaction between the farmers and the other network actors, including scientists, was essential for farmer innovation. New relationships emerged between the scientists and farmers, based on experimental learning and the co-production of knowledge for improving laying hen welfare. The project demonstrated that a practice-led approach can be a major stimulus for innovation with several networks generating novel ideas and testing them in their commercial context. The Hennovation innovation networks not only contributed to bridging the science-practice gap by application of existing scientific solutions in practice but more so by jointly finding new solutions. Successful multi-actor, practice-led innovation networks appeared to depend upon the following key factors: active participation from relevant actors, professional facilitation, moderate resource support and access to relevant expertise. Farmers and processors involved in the project were often very enthusiastic about the approach, committing significant time to the network’s activities. It is suggested that the agricultural research community and funding agencies should place greater value on practice-led multi-actor innovation networks alongside technology and advisor focused initiatives to improve animal welfare and embed best practices

Parameters identification of unknown delayed genetic regulatory networks by a switching particle swarm optimization algorithm

The official published version can be found at the link below.This paper presents a novel particle swarm optimization (PSO) algorithm based on Markov chains and competitive penalized method. Such an algorithm is developed to solve global optimization problems with applications in identifying unknown parameters of a class of genetic regulatory networks (GRNs). By using an evolutionary factor, a new switching PSO (SPSO) algorithm is first proposed and analyzed, where the velocity updating equation jumps from one mode to another according to a Markov chain, and acceleration coefficients are dependent on mode switching. Furthermore, a leader competitive penalized multi-learning approach (LCPMLA) is introduced to improve the global search ability and refine the convergent solutions. The LCPMLA can automatically choose search strategy using a learning and penalizing mechanism. The presented SPSO algorithm is compared with some well-known PSO algorithms in the experiments. It is shown that the SPSO algorithm has faster local convergence speed, higher accuracy and algorithm reliability, resulting in better balance between the global and local searching of the algorithm, and thus generating good performance. Finally, we utilize the presented SPSO algorithm to identify not only the unknown parameters but also the coupling topology and time-delay of a class of GRNs.This research was partially supported by the National Natural Science Foundation of PR China (Grant No. 60874113), the Research Fund for the Doctoral Program of Higher Education (Grant No. 200802550007), the Key Creative Project of Shanghai Education Community (Grant No. 09ZZ66), the Key Foundation Project of Shanghai (Grant No. 09JC1400700), the Engineering and Physical Sciences Research Council EPSRC of the UK under Grant No. GR/S27658/01, the International Science and Technology Cooperation Project of China under Grant No. 2009DFA32050, an International Joint Project sponsored by the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany