Face-to-face or distance training? Two different approaches to motivate SMEs to learn - an update

In the past, too many government-sponsored initiatives have presented learning resources that have been wasted because the target small business audience has failed to make use of them. This paper explores the issue of offering learning materials to small and medium-sized enterprises (SMEs) in a manner that recognizes their working environment, mode of operation and preferred learning methods. It then outlines methods currently being tested in the UK and Ireland, and indicates preliminary findings. The two methodologies are different in that the UK (LSSB - Learning Support for Small Businesses) programme is aimed at distance learning in primarily small businesses, whilst the Irish (University of Limerick and Limerick City Enterprise Board) programme is aimed at face-to-face learning primarily in micro-enterprises. Preliminary findings are presented

Optimizing the performance of thermionic devices using energy filtering

Conventional thermionic power generators and refrigerators utilize a barrier in the direction of transport to selectively transmit high-energy electrons. Here we show that the energy spectrum of electrons transmitted in this way is not optimal, and we derive the ideal energy spectrum for operation in the maximum power regime. By using suitable energy filters, such as resonances in quantum dots, the power of thermionic devices can, in principle, be improved by an order of magnitude.Comment: 3 pages, 2 figure

Automatic Scenario Generation for Robust Optimal Control Problems

Existing methods for nonlinear robust control often use scenario-based approaches to formulate the control problem as nonlinear optimization problems. Increasing the number of scenarios improves robustness while increasing the size of the optimization problems. Mitigating the size of the problem by reducing the number of scenarios requires knowledge about how the uncertainty affects the system. This paper draws from local reduction methods used in semi-infinite optimization to solve robust optimal control problems with parametric uncertainty. We show that nonlinear robust optimal control problems are equivalent to semi-infinite optimization problems and can be solved by local reduction. By iteratively adding interim globally worst-case scenarios to the problem, methods based on local reduction provide a way to manage the total number of scenarios. In particular, we show that local reduction methods find worst-case scenarios that are not on the boundary of the uncertainty set. The proposed approach is illustrated with a case study with both parametric and additive time-varying uncertainty. The number of scenarios obtained from local reduction is 101, smaller than in the case when all 2 14+3×192 boundary scenarios are considered. A validation with randomly-drawn scenarios shows that our proposed approach reduces the number of scenarios and ensures robustness even if local solvers are used

Automatic scenario generation for efficient solution of robust optimal control problems

Existing methods for nonlinear robust control often use scenario-based approaches to formulate the control problem as large nonlinear optimization problems. The optimization problems are challenging to solve due to their size, especially if the control problems include time-varying uncertainty. This paper draws from local reduction methods used in semi-infinite optimization to solve robust optimal control problems with parametric and time-varying uncertainty. By iteratively adding interim worst-case scenarios to the problem, methods based on local reduction provide a way to manage the total number of scenarios. We show that the local reduction method for optimal control problems consists of solving a series of simplified optimal control problems to find worst-case constraint violations. In particular, we present examples where local reduction methods find worst-case scenarios that are not on the boundary of the uncertainty set. We also provide bounds on the error if local solvers are used. The proposed approach is illustrated with two case studies with parametric and additive time-varying uncertainty. In the first case study, the number of scenarios obtained from local reduction is 101, smaller than in the case when all 2¹⁴⁺³×¹⁹² extreme scenarios are considered. In the second case study, the number of scenarios obtained from the local reduction is two compared to 512 extreme scenarios. Our approach was able to satisfy the constraints both for parametric uncertainty and time-varying disturbances, whereas approaches from literature either violated the constraints or became computationally expensive

Automatic scenario generation for efficient solution of robust optimal control problems

Existing methods for nonlinear robust control often use scenario-based approaches to formulate the control problem as large nonlinear optimization problems. The optimization problems are challenging to solve due to their size, especially if the control problems include time-varying uncertainty. This paper draws from local reduction methods used in semi-infinite optimization to solve robust optimal control problems with parametric and time-varying uncertainty. By iteratively adding interim worst-case scenarios to the problem, methods based on local reduction provide a way to manage the total number of scenarios. We show that the local reduction method for optimal control problems consists of solving a series of simplified optimal control problems to find worst-case constraint violations. In particular, we present examples where local reduction methods find worst-case scenarios that are not on the boundary of the uncertainty set. We also provide bounds on the error if local solvers are used. The proposed approach is illustrated with two case studies with parametric and additive time-varying uncertainty. In the first case study, the number of scenarios obtained from local reduction is 101, smaller than in the case when all 2¹⁴+³ₓ¹⁹² extreme scenarios are considered. In the second case study, the number of scenarios obtained from the local reduction is two compared to 512 extreme scenarios. Our approach was able to satisfy the constraints both for parametric uncertainty and time-varying disturbances, whereas approaches from literature either violated the constraints or became computationally expensive

A Visit to Bokhara in 1919: Discussion

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Wastewater-to-resource- Design of a sustainable phosphorus recovery system

To enable more sustainable wastewater treatment processes, a transition towards resource recovery methods that have minimal environmental impact while being financially viable is imperative. Phosphorus (P) is a finite resource that is being discharged into the aqueous environment in excessive quantities. As such, understanding the financial and environmental effectiveness of different approaches for removing and recovering P from wastewater streams is important to reduce the overall impact of wastewater treatment. In this study, a process-systems modelling framework for comprehensively evaluating these approaches in terms of both economic and environmental impacts is developed. Applying this framework, treatment pathways are designed, simulated and analysed to determine the most suitable approaches for P removal and recovery. The purpose of this methodology is not only to assist with plant design, but also to identify the principal economic and environmental factors acting as barriers to implementing a given technology, incorporating the impact of waste recovery. The results suggest that the chemical and ion-exchange approaches studied deliver sustainable advantages over biological pathways, both economically and environmentally, with each possessing different strengths. The assessment methodology developed enables a more rational and environmentally sound wastewater plant design approach to be taken

Post-operative immune suppression is reversible with interferon gamma and independent of IL-6 pathways

Introduction The post-operative period is characterised by increased IL-6 production and clinical features of immune suppression. In vitro anti-inflammatory actions of IL-6 are mediated through suppression of interferon gamma (IFNγ) [1]. The clinical significance of IL-6 in mediating post-operative immune suppression remains unclear. Objectives To evaluate the role of IL-6 pathways in post-operative immune suppression and the reversibility of this phenomenon. Methods Patients over 45 years old undergoing elective surgery involving the gastrointestinal tract and requiring at least an overnight hospital stay were recruited. The primary outcome was hospital-acquired infection. IL-6 and IFNγ levels were assayed using ELISA preoperatively and at 24 and 48 hours. Pooled healthy control peripheral blood mononuclear cells (PBMCs) were cultured in perioperative serum and CD14+HLA-DR (mHLA-DR) geometric mean florescent intensity (MFI) measured in the presence and absence of interferon gamma (IFNγ) and IL-6 neutralising antibody. Data were analysed with non-parametric statistics. Results 119 patients were recruited and 44 (37%) developed a post-operative infection a median of 9 (IQR 5-11) days postoperatively (Figure 1). IL-6 levels increased from baseline to 24 hours postoperatively (P < 0.0001, Figure 1A) but were then unchanged between 24 and 48 hours (P = 0.06, Figure 1B). Postoperative IL-6 levels correlated with the duration of the procedure (P = 0.009). Higher preoperative IL-6 levels were observed in patients with cancer (P = 0.02). IL-6 levels at 24 (P = 0.0002) and 48 hours (P = 0.003) were associated with the later occurrence of infectious complications. This pattern remained similar after adjustment for baseline characteristics. Healthy donor PBMCs incubated with postoperative serum downregulated mHLA-DR MFI when compared with serum from baseline (n = 8, p = 0.008). Culturing in the presence of IFNγ 250IU (n = 4) prevented this decrease whereas culturing in the presence of IL-6 neutralising antibody 15ng/ml (n = 8) did not. Conclusions IL-6 levels increase following major surgery and are associated with an increased susceptibility to post-operative infections. Serum obtained from post-operative patients induces an immunosuppressive response through an IL-6 independent pathways which is reversible with IFNγ treatment

Data-driven predictive control with reduced computational effort and improved performance using segmented trajectories

A class of data-driven control methods has recently emerged based on Willems' fundamental lemma. Such methods can ease the modelling burden in control design but can be sensitive to disturbances acting on the system under control. In this paper, we extend these methods to incorporate segmented prediction trajectories. The proposed segmentation enables longer prediction horizons to be used in the presence of unmeasured disturbance. Furthermore, a computation time reduction can be achieved through segmentation by exploiting the problem structure, with computation time scaling linearly with increasing horizon length. The performance characteristics are illustrated in a set-point tracking case study in which the segmented formulation enables more consistent performance over a wide range of prediction horizons. The computation time for the segmented formulation is approximately half that of an unsegmented formulation for a horizon of 100 samples. The method is then applied to a building energy management problem, using a detailed simulation environment, in which we seek to minimise the discomfort and energy of a 6-room apartment. With the segmented formulation, a 72% reduction in discomfort and 5% financial cost reduction is achieved, compared to an unsegmented formulation using a one-day-ahead prediction horizon