The stress process in palliative cancer care:A qualitative study on informal caregiving and its implication for the delivery of care

Objective: The primary objective of this study was to examine how the comprehensive nature of the Stress Process Model could elucidate on the stressors associated with caring for a palliative cancer patient. Method: A qualitative research strategy involving home-based face-to-face interviews with 12 bereaved family caregivers was used to examine the caregiving experience. Results: The primary stressors associated with caring for the palliative cancer care patients stemmed from care recipient symptoms and personal care needs. The absence of adequate support from the formal health care delivery system was a consistent message from all participants. There was evidence of financial stress primarily associated with the purchase of private home care to supplement formal care. In contrast, the resources that family caregivers relied on to moderate the stressful effects of caregiving included extended family, friends, and neighbors. While the stress of direct caregiving was high, the study revealed that formal care was also a significant source of stress for family caregivers. Conclusion: It was concluded that an appropriately financed, integrated system of care that followed a person-centered philosophy of care would best meet the needs of the patient and his or her family. © The Author(s) 2010.

Stress processes in caring for an end-of-life family member:Application of a theoretical model

Objectives: Family caregivers play a vital role in maintaining the lives of individuals with advanced illness living in the community. However, the responsibility of caregiving for an end-of-life family member can have profound consequences on the psychological, physical and financial well-being of the caregiver. While the literature has identified caregiver stress or strain as a complex process with multiple contributing factors, few comprehensive studies exist. This study examined a wide range of theory-driven variables contributing to family caregiver stress. Method: Data variables from interviews with primary family caregivers were mapped onto the factors within the Stress Process Model theoretical framework. A hierarchical multiple linear regression analysis was used to determine the strongest predictors of caregiver strain as measured by a validated composite index, the Caregiver Strain Index. Results: The study included 132 family caregivers across south-central/western Ontario, Canada. About half of these caregivers experienced high strain, the extent of which was predicted by lower perceived program accessibility, lower functional social support, greater weekly amount of time caregivers committed to the care recipient, younger caregiver age and poorer caregiver self-perceived health. Conclusion: This study examined the influence of a multitude of factors in the Stress Process Model on family caregiver strain, finding stress to be a multidimensional construct. Perceived program accessibility was the strongest predictor of caregiver strain, more so than intensity of care, highlighting the importance of the availability of community resources to support the family caregiving role.

Special condition wind power forecasting based on Gaussian process and similar historical data

Due to the variability of wind power, it is imperative to accurately and timely forecast the wind generation to enhance the flexibility and reliability of the operation and control of real-time power. Special events such as ramps, spikes are hard to predict with traditional methods using solely recently measured data. In this paper, a new Gaussian Process model with hybrid training data taken from both the local time and historic dataset is proposed and applied to make short-term predictions from 10 minutes to one hour ahead. A key idea is that the similar pattern data in history are properly selected and embedded in Gaussian Process model to make predictions. The results of the proposed algorithms are compared to those of standard Gaussian Process model and the persistence model. It is shown that the proposed method not only reduces magnitude error but also phase error.

An optimized process design for oxymethylene ether production from woody-biomass-derived syngas

The conversion of biomass for the production of liquid fuels can help reduce the greenhouse gas (GHG) emissions that are predominantly generated by the combustion of fossil fuels. Oxymethylene ethers (OMEs) are a series of liquid fuel additives that can be obtained from syngas, which is produced from the gasification of biomass. The blending of OMEs in conventional diesel fuel can reduce soot formation during combustion in a diesel engine. In this research, a process for the production of OMEs from woody biomass has been simulated. The process consists of several unit operations including biomass gasifi- cation, syngas cleanup, methanol production, and conversion of methanol to OMEs. The methodology involved the development of process models, the identification of the key process parameters affecting OME production based on the process model, and the development of an optimal process design for high OME yields. It was found that up to 9.02 tonnes day1 of OME3, OME4, and OME5 (which are suitable as diesel additives) can be produced from 277.3 tonnes day1 of wet woody biomass. Furthermore, an optimal combination of the parameters, which was generated from the developed model, can greatly enhance OME production and thermodynamic efficiency. This model can further be used in a techno- economic assessment of the whole biomass conversion chain to produce OMEs. The results of this study can be helpful for petroleum-based fuel producers and policy makers in determining the most attractive pathways of converting bio-resources into liquid fuels.

Time series wind power forecasting based on variant Gaussian process and TLBO

Due to the variability and stochastic nature of wind power system, accurate wind power forecasting has an important role in developing reliable and economic power system operation and control strategies. As wind variability is stochastic, Gaussian Process regression has recently been introduced to capture the randomness of wind energy. However, the disadvantages of Gaussian Process regression include its computation complexity and incapability to adapt to time varying time-series systems. A variant Gaussian Process for time series forecasting is introduced in this study to address these issues. This new method is shown to be capable of reducing computational complexity and increasing prediction accuracy. It is further proved that the forecasting result converges as the number of available data approaches innite. Further, a teaching learning based optimization (TLBO) method is used to train the model and to accelerate
the learning rate. The proposed modelling and optimization method is applied to forecast both the wind power generation of Ireland and that from a single wind farm to show the eectiveness of the proposed method.

Exploring the adoption and implementation of discrete and integrated IT systems: A Framework for analysis

Despite the substantial organisational benefits of integrated IT, the implementation of such systems – and particularly Enterprise Resource Planning (ERP) systems – has tended to be problematic, stimulating an extensive body of research into ERP implementation. This research has remained largely separate from the main IT implementation literature. At the same time, studies of IT implementation have generally adopted either a factor or process approach; both have major limitations. To address these imitations, factor and process perspectives are combined here in a unique model of IT implementation. We argue that • the organisational factors which determine successful implementation differ for integrated and traditional, discrete IT • failure to manage these differences is a major source of integrated IT failure. The factor/process model is used as a framework for proposing differences between discrete and integrated IT.

Linking watershed terrain and hydrology to soil chemical properties, microbial communities and impacts on soil organic C in a humid mid-latitude forested watershed

Understanding the response of humid mid-latitude forests to changes in precipitation, temperature, nutrient cycling, and disturbance is critical to improving our predictive understanding of changes in the surface-subsurface energy balance due to climate change. Mechanistic understanding of the effects of long-term and transient moisture conditions are needed to quantify
linkages between changing redox conditions, microbial activity, and soil mineral and nutrient interactions on C cycling and greenhouse gas releases. To illuminate relationships between the soil chemistry, microbial communities and organic C we established transects across hydraulic and topographic gradients in a small watershed with transient moisture conditions. Valley bottoms tend to be more frequently saturated than ridge tops and side slopes which generally are only saturated when shallow storm flow zones are active. Fifty shallow (~36”) soil cores were collected during timeframes representative of low CO2, soil winter conditions and high CO2, soil summer conditions. Cores were subdivided into 240 samples based on pedology and analyses of the geochemical (moisture content, metals, pH, Fe species, N, C, CEC, AEC) and microbial (16S rRNA gene
amplification with Illumina MiSeq sequencing) characteristics were conducted and correlated to watershed terrain and hydrology. To associate microbial metabolic activity with greenhouse gas emissions we installed 17 soil gas probes, collected gas samples for 16 months and analyzed them for CO2 and other fixed and greenhouse gasses. Parallel to the experimental efforts our data is being used to support hydrobiogeochemical process modeling by coupling the Community Land Model (CLM) with a subsurface process model (PFLOTRAN) to simulate processes and interactions from the molecular to watershed scales. Including above ground processes (biogeophysics, hydrology, and vegetation dynamics), CLM provides mechanistic water, energy, and organic matter inputs to the surface/subsurface models, in which coupled biogeochemical reaction
networks are used to improve the representation of below-ground processes. Preliminary results suggest that inclusion of above ground processes from CLM greatly improves the prediction of moisture response and water cycle at the watershed scale.

QA and Enhancement Marketplace for HEIs – An Erasmus+ project

Improving European education and training system quality has been set as a key target in Europe’s strategy to become a smart, sustainable and inclusive economy by 2020 (European Commission, 2010). These objectives are more specifically defined in the so called Modernisation Agenda (European Commission, 2011). More specifically it sets a goal to improve the quality and relevance of higher education. In this process external evaluation and
Proceedings of the 11th International CDIO Conference, Chengdu University of Information Technology,
Chengdu, Sichuan, P.R. China, June 8-11, 2015.
self-assessment are seen in a key role! In the CDIO approach the 12 CDIO standards provide a framework for continuous improvement. Each institution/institutional department are encouraged to regularly do the self-evaluation using the CDIO Standards. Eight European universities identified a need for further enhancement of the self-evaluations and creation of processes with peers to reduce the inertia of heavy accreditations/evaluations in HEIs. In September 2014 these universities started an Erasmus+ project (QAEMarketPlace4HEI) aiming at
1. Developing a collaborative, comprehensive and accessible evaluation process model, methods and tools for HEIs to complement the accreditation systems.
2. Promoting, increasing and exploiting further the European collaboration in the evaluation processes and the exchange of best practices.
3. Disseminating the model, best practices and widen the cooperation to new HEIs in Europe through the partner networks.

Learning, fatigue and preference formation in discrete choice experiments

While the repeated nature of Discrete Choice Experiments is advantageous from a sampling efficiency perspective, patterns of choice may differ across the tasks, due, in part, to learning and fatigue. Using probabilistic decision process models, we find in a field study that learning and fatigue behavior may only be exhibited by a small subset of respondents. Most respondents in our sample show preference and variance stability consistent with rational pre-existent and
well formed preferences. Nearly all of the remainder exhibit both learning and fatigue effects. An important aspect of our approach is that it enables learning and fatigue effects to be explored, even though they were not envisaged during survey design or data collection.