Background: A diagnosis of cancer causes physical and psychological implications that continue throughout the cancer treatment trajectory. Psychological distress, including depression, is one of the most common negative effects experienced by adults with cancer and often remains untreated despite positive screening. Psychosocial interventions are an appropriate method to address depression and can be implemented in the acute care setting, outpatient clinics, or patients’ homes by a registered nurse. Methods: A comprehensive search of CINAHL, MEDLINE, and APA PsycINFO was conducted, and articles were screened for appropriateness. A total of eight randomized controlled trials that met inclusion criteria and contained a rigorous methodological design were analyzed. Results: Implementation of nurse-led psychosocial interventions in adults with cancer indicated a positive correlation in the mitigation of depressive symptoms; however, feasibility (defined as an adherence rate of greater than 80% in the intervention group) remains inconclusive. While four of the articles analyzed showed promise of the feasibility of a nurse-led psychosocial intervention, the remaining articles did not include enough detail about adherence to analyze. Conclusions: To transition nurse-led psychosocial interventions into clinical practice, more primary research documenting strict adherence and withdrawal rates must be conducted to determine feasibility. Additional analysis should seek to examine definitive training required by registered nurses prior to implementation to account for resource barriers that may impact success of this type of intervention in the clinical setting.

This work is motivated by the pressing need for operational control in the fabrication of Microelectromechanical systems or MEMS. MEMS are miniature three-dimensional integrated electromechanical systems with the ability to absorb information from the environment, process this information and suitably react to it. These devices offer tremendous advantages owing to their small size, low power consumption, low mass and high functionality, which makes them very attractive in applications with stringent demands on weight, functionality and cost. While the system''s "brain" (device electronics) is fabricated using traditional IC technology, the micromechanical components necessitate very intricate and sophisticated processing of silicon or other suitable substrates. A dearth of fabrication facilities with micromachining capabilities and a lengthy gestation period from design to mass fabrication and commercial acceptance of the product in the market are factors most often implicated in hampering the growth of MEMS. These devices are highly application specific with low production volumes and the few fabs that do possess micromachining capabilities are unable to offer a complete array of fabrication processes in order to be able to cater to the needs of the MEMS R&D community. A distributed fabrication network has, therefore, emerged to serve the evolving needs of this high investment, low volume MEMS industry. Under this environment, a central facility coordinates between a network of fabrication centers (Network of MEMS job shops -- NMJS) containing micromachining capabilities. These fabrication centers include commercial, academic and government fabs, which make their services available to the ordinary customer. Wafers are shipped from one facility to another until all processing requirements are met. The lengthy and intricate process sequences that need to be performed over a network of capital intensive facilities are complicated by dynamic job arrivals, stochastic processing times, sequence-dependent set ups and travel between fabs. Unless the production of these novel devices is carefully optimized, the benefits of distributed fabrication could be completely overshadowed by lengthy lead times, chaotic routings and costly processing. Our goal, therefore, is to develop and validate an approach for optimal routing (assignment) and sequencing of MEMS devices in a network of stochastic job shops with the objective of minimizing the sum of completion times and the cost incurred, given a set of fabs, machines and an expected product mix. In view of our goal, we begin by modeling the stochastic NMJS problem as a two-stage stochastic program with recourse where the first-stage variables are binary and the second-stage variables are continuous. The key decision variables are binary and pertain to the assignment of jobs to machines and their sequencing for processing on the machines. The assignment variables essentially fix the route of a job as it travels through the network because these variables specify the machine on which each job-operation must be performed out of several candidate machines. Once the assignment is decided upon, sequencing of job-operations on each machine follows. The assignment and sequencing must be such that they offer the best solution (in terms of the objective) possible in light of all the processing time scenarios that can be realized. We present two approaches for solving the stochastic NMJS problem. The first approach is based on the L-shaped method (credited to van Slyke and Wets, 1969). Since the NMJS problem lacks relatively complete recourse, the first-stage solution can be infeasible to the second-stage problem in that the first stage solution may either violate the reentrant flow conditions or it may create a deadlock. In order to alleviate these infeasibilities, we develop feasibility cuts which when appended to the master problem eliminate the infeasible solution. Alternatively, we also develop constraints to explicitly address these infeasibilities directly within the master problem. We show how a deadlock involving 2 or 3 machines arises if and only if a certain relationship between operations and a certain sequence amongst them exists. We generalize this argument to the case of m machines, which forms the basis for our deadlock prevention constraints. Computational results at the end of Chapter 3 compare the relative merits of a model which relies solely on feasibility cuts with models that incorporate reentrant flow and deadlock prevention constraints within the master problem. Experimental evidence reveals that the latter offers appreciable time savings over the former. Moreover, in a majority of instances we see that models that carry deadlock prevention constraints in addition to the reentrant flow constraints provide at par or better performance than those that solely carry reentrant flow constraints. We, next, develop an optimality cut which when appended to the master problem helps in eliminating the suboptimal master solution. We also present alternative optimality and feasibility cuts obtained by modifying the disjunctive constraints in the subproblem so as to eliminate the big H terms in it. Although any large positive number can be used as the value of H, a conservative estimate may improve computational performance. In light of this, we develop a conservative upper bound for operation completion times and use it as the value of H. Test instances have been generated using a problem generator written in JAVA. We present computational results to evaluate the impact of a conservative estimate for big H on run time, analyze the effect of the different optimality cuts and demonstrate the performance of the multicut method (Wets, 1981) which differs from the L-shaped method in that the number of optimality cuts it appends is equal to the number of scenarios in each iteration. Experimentation indicates that Model 2, which uses the standard optimality cut in conjunction with the conservative estimate for big H, almost always outperforms Model 1, which also uses the standard optimality cut but uses a fixed value of 1000 for big H. Model 3, which employs the alternative optimality cut with the conservative estimate for big H, requires the fewest number of iterations to converge to the optimum but it also incurs the maximum premium in terms of computational time. This is because the alternative optimality cut adds to the complexity of the problem in that it appends additional variables and constraints to the master as well as the subproblems. In the case of Model 4 (multicut method), the segregated optimality cuts accurately reflect the shape of the recourse function resulting in fewer overall iterations but the large number of these cuts accumulate over the iterations making the master problem sluggish and so this model exhibits a variable performance for the various datasets. These experiments reveal that a compact master problem and a conservative estimate for big H positively impact the run time performance of a model. Finally, we develop a framework for a branch-and-bound scheme within which the L-shaped method, as applied to the NMJS problem, can be incorporated so as to further enhance its performance. Our second approach for solving the stochastic NMJS problem relies on the tight LP relaxation observed for the deterministic equivalent of the model. We, first, solve the LP relaxation of the deterministic equivalent problem, and then, fix certain binary assignment variables that take on a value of either a 0 or a 1 in the relaxation. Based on this fixing of certain assignment variables, additional logical constraints have been developed that lead to the fixing of some of the sequencing variables too. Experimental results, comparing the performance of the above LP heuristic procedure with CPLEX over the generated test instances, illustrate the effectiveness of the heuristic procedure. For the largest problems (5 jobs, 10 operations/job, 12 machines, 7 workcenters, 7 scenarios) solved in this experiment, an average savings of as much as 4154 seconds and 1188 seconds was recorded in a comparison with Models 1 and 2, respectively. Both of these models solve the deterministic equivalent of the stochastic NMJS problem but differ in that Model 1 uses a big H value of 1000 whereas Model 2 uses the conservative upper bound for big H developed in this work. The maximum optimality gap observed for the LP heuristic over all the data instances solved was 1.35%. The LP heuristic, therefore, offers a powerful alternative to solving these problems to near-optimality with a very low computational burden. We also present results pertaining to the value of the stochastic solution for various data instances. The observed savings of up to 8.8% over the mean value approach underscores the importance of using a solution that is robust over all scenarios versus a solution that approximates the randomness through expected values. We, next, present a dynamic stochastic scheduling approach (DSSP) for the NMJS problem. The premise behind this undertaking is that in a real-life implementation that is faithful to the two-stage procedure, assignment (routing) and sequencing decisions will be made for all the operations of all the jobs at the outset and these will be followed through regardless of the actual processing times realized for individual operations. However, it may be possible to refine this procedure if information on actual processing time realizations for completed operations could be utilized so that assignment and sequencing decisions for impending operations are adjusted based on the evolving scenario (which may be very different from the scenarios modeled) while still hedging against future uncertainty. In the DSSP approach, the stochastic programming model for the NMJS problem is solved at each decision point using the LP heuristic in a rolling horizon fashion while incorporating constraints that model existing conditions in the shop floor and the actual processing times realized for the operations that have been completed. The implementation of the DSSP algorithm is illustrated through an example problem. The results of the DSSP approach as applied to two large problem instances are presented. The performance of the DSSP approach is evaluated on three fronts; first, by using the LP heuristic at each decision point, second, by using an optimal algorithm at each decision point, and third, against the two-stage stochastic programming approach. Results from the experimentation indicate that the DSSP approach using the LP heuristic at each decision point generates superior assignment and sequencing decisions than the two-stage stochastic programming approach and provides solutions that are near-optimal with a very low computational burden. For the first instance involving 40 operations, 12 machines and 3 processing time scenarios, the DSSP approach using the LP heuristic yields the same solution as the optimal algorithm with a total time savings of 71.4% and also improves upon the two-stage stochastic programming solution by 1.7%. In the second instance, the DSSP approach using the LP heuristic yields a solution with an optimality gap of 1.77% and a total time savings of 98% over the optimal algorithm. In this case, the DSSP approach with the LP heuristic improves upon the two-stage stochastic programming solution by 6.38%. We conclude by presenting a framework for the DSSP approach that extends the basic DSSP algorithm to accommodate jobs whose arrival times may not be known in advance. / Ph. D.

Over the last two decades, livestock operations have become highly concentrated due to growing trends towards larger, more confined facilities and a decrease in cropland on smaller farms. This has led to greater amounts of excess manure nutrients on farms, increasing the potential for nutrient pollution of water bodies from runoff. The purpose of this study was to determine if enhanced biological phosphorus removal (EBPR) is a viable alternative for managing excess manure nutrients on dairy farms. Assessment of EBPR involved the investigation of various aspects of wastewater treatment modeling and design and farm nutrient management. The fermentation potential (volatile fatty acid (VFA) production) of dairy manure was determined through laboratory analysis to be 15.3% of the total COD. Total VFA production was composed of 57, 23, and 20% acetic, propionic, and butyric acids, respectively. The EBPR component of the BioWin wastewater treatment model was evaluated through a sensitivity analysis. The parameters to which effluent phosphate (PO4) concentration was most sensitive were maximum specific growth rate, growth yield, aerobic PO4 uptake rate per unit poly-b-hydroxybutyrate (PHB) utilized, PHB yield from VFA, PO4 release per unit VFA uptake, and fraction of releasable PO4. An EBPR sequencing batch reactor (SBR) was designed for a dairy farm with 700 lactating cows and 325 ha of corn silage. An economic analysis of EBPR for dairy farms employing P-based manure applications was completed. The cost of hauling excess manure to nutrient deficient farms was the most significant expense in comparing costs of manure management with and without EBPR. For a herd of 700 lactating cows, utilizing EBPR was more economical for farms with 270 ha or less cropland, while EBPR did not offer an economic advantage for farms over 270 ha. / Master of Science

Yes / prevent obesity at this age. This study tested the acceptability and feasibility of evaluating a theory-based intervention aimed at reducing risk of obesity in infants of overweight/obese women during and after pregnancy: the Healthy and Active Parenting Programme for Early Years (HAPPY). Methods: A feasibility randomised controlled trial was conducted in Bradford, England. One hundred twenty overweight/obese pregnant women (Body Mass Index [BMI] ≥25 kg/m2) were recruited between 10–26 weeks gestation. Consenting women were randomly allocated to HAPPY (6 antenatal, 6 postnatal sessions: N = 59) or usual care (N = 61). Appropriate outcome measures for a full trial were explored, including: infant’s length and weight, woman’s BMI, physical activity and dietary intake of the women and infants. Health economic data were collected. Measurement occurred before randomisation and when the infant was aged 6 months and 12 months. Feasibility outcomes were: recruitment/attrition rates, and acceptability of: randomisation, measurement, and intervention. Intra-class correlations for infant weight were calculated. Fidelity was assessed through observations and facilitator feedback. Focus groups and semi-structured interviews explored acceptability of methods, implementation, and intervention content. Results: Recruitment targets were met (~20 women/month) with a recruitment rate of 30 % of eligible women (120/396). There was 30 % attrition at 12 months; 66 % of recruited women failed to attend intervention sessions, but those who attended the first session were likely to continue to attend (mean 9.4/12 sessions, range 1–12). Reaction to intervention content was positive, and fidelity was high. Group clustering was minimal; an adjusted effect size of −0.25 standard deviation scores for infant weight at 12 months (95 % CI: −0.16–0.65) favouring the intervention was observed using intention to treat analyses. No adverse events were reported. Conclusions: The HAPPY intervention appeared feasible and acceptable to participants who attended and those delivering it, however attendance was low; adaptations to increase initial attendance are recommended. Whilst the study was not powered to detect a definitive effect, our results suggest a potential to reduce risk of infant obesity. The evidence reported provides valuable lessons to inform progression to a definitive trial. / National Institute for Health Research

Yes / Fidelity to the treatment protocol is key to successful trials but often problematic. This article reports the staff's views on delivering a complex rehabilitation intervention: patient-led therapy during inpatient stroke care. An exploratory qualitative study using focus groups with staff involved in a multicenter (n = 12) feasibility trial of patient-led therapy (the MAESTRO trial) was undertaken as part of the evaluation process. Purposive sampling ensured that participants represented all recruiting sites, relevant professions and levels of seniority. Data analysis used a Framework Approach. Five focus groups were held involving 30 participants. Five main themes emerged: the effect of the interventions, practical problems, patient-related factors, professional dilemmas, and skills. Staff felt the main effect of the therapies was on patients' autonomy and occupation; the main practical problems were the patients' difficulties in achieving the correct position and a lack of space. Staff clearly identified characteristics that made patient-led therapy unsuitable for some patients. Most staff experienced dilemmas over how to prioritize the trial interventions compared to their usual therapy and other clinical demands. Staff also lacked confidence about how to deliver the interventions, particularly when adapting the interventions to individual needs. For each barrier to implementation, possible solutions were identified. Of these, involving other people and establishing a routine were the most common. Delivering rehabilitation interventions within a trial is complex. Staff require time and support to develop the skills, strategies and confidence to identify suitable patients, deliver new treatments, adapt the new treatments to individuals' needs and balance the demands of delivering the trial intervention according to the treatment protocol with other clinical and professional priorities. TRIAL REGISTRATION: ISRCTN: ISRCTN29533052 . October 2011.

Yes / Sepsis is one of the most common causes of mortality in postnatal women globally and many other women who develop sepsis are left with severe morbidity. Women’s knowledge of postnatal sepsis and how it can be prevented by simple changes to behaviour is lacking. Methods: This paper describes the co-development and feasibility testing of a digital animation intervention called DAISI (digital animation in service improvement). This DAISI is designed to enhance postnatal women’s awareness of sepsis and how to reduce their risk of developing the condition. We co-designed the digital animation over a six-month period underpinned by theory, best evidence and key stakeholders, translated it into Urdu then assessed its use, firstly in a focus group with women from different Black, Asian and Minority Ethnic (BAME) groups and secondly with 15 clinical midwives and 15 women (including BAME women). Following exposure to the intervention, midwives completed a questionnaire developed from the COM-B behaviour change model and women participated in individual and focus group interviews using similar questions. Results: The animation was considered acceptable, culturally sensitive and simple to implement and follow. Discussion: DAISI appears to be an innovative solution for use in maternity care to address difficulties with the postnatal hospital discharge process. We could find no evidence of digital animation being used in this context and recommend a study to test it in practice prior to adopting its use more widely. If effective, the DAISI principle could be used in other maternity contexts and other areas of the NHS to communicate health promotion information. / This study was funded by Translate Medtech, Leeds City Region. DAISI was developed by HMA Digital, Barnsley, UK.

Yes / The family environment plays a crucial role in child physical activity (PA). Wearable activity trackers (wearables) show potential for increasing children's PA; however, few studies have explored families' acceptance of wearables. This study investigated the acceptability of using wearables in a family setting, aligning experiences with components of the Technology Acceptance Model and Theoretical Domains Framework. Twenty-four families, with children aged 5-9 years, took part in a 5-week study, where all members were provided with a Fitbit Alta HR for 4 weeks. Acceptability was measured using weekly surveys and pre-post-questionnaires. Nineteen families participated in a focus group. Quantitative and qualitative data were integrated using the Pillar Integration Process technique. Pillars reflected (1) external variables impacting wearable use and PA and (2) wearable use, (3) ease of use, (4) usefulness for increasing PA and other health outcomes, (5) attitudes, and (6) intention to use a wearable, including future intervention suggestions. Families found the Fitbit easy to use and acceptable, but use varied, and perceived impact on PA were mixed, with external variables contributing towards this. This study provides insights into how wearables may be integrated into family-based PA interventions and highlights barriers and facilitators of family wearable use. / This study is funded as part of a Ph.D. studentship by the Born in Bradford study. The Born in Bradford study receives core infrastructure funding from the Wellcome Trust (WT101597MA) and the National Institute for Health Research (NIHR), under its NIHR ARC Yorkshire and Humber (NIHR200166) and Clinical Research Network (CRN) research delivery support. For this piece of work, funding from the Sport England’s Local Delivery Pilot awarded Born in Bradford funding for this Ph.D. studentship. S.A.C. is supported by the NIHR Leicester Biomedical Research Centre—Lifestyle theme

No / Newcastle Upon Tyne Hospitals NHS Foundation Trust

The subject of the diploma thesis is to acquaint the reader with the issue of a public investment project and then apply the information found to the case study in the form of a financial and economic evaluation of the selected investment plan. The evaluated investment project is the reconstruction of a cinema in the village of Hustopeče in the South Moravian Region, while the identified outputs were achieved using the cost method of cost-benefit analysis using the eCBA application. Part of the evaluation also includes a risk analysis in the form of a sensitivity and qualitative analysis.

This thesis is the result of a collaboration between Ericsson AB and Luleå University of Technology. A feasibility study has been conducted to investigate antenna options for the HIBS access link. The study contains two parts. Firstly, a link budget investigating the gain required from the antenna. The metric of concern in the link budget was SNR. Secondly, a wide area coverage investigation that explored coverage feasibility over an area with a radius of 100 km. The metrics of concern in this investigation were antenna gain and beamwidth. Two types of antennas have been included: parabolic reflector and phased array. Seven frequency bands have been studied: 0.7, 1.9, 2.7, 3.5, 6, 10, and 26 GHz. The first three bands shared a bandwidth of 20 MHz, the next three shared a bandwidth of 80 MHz, and the last band had a bandwidth of 100 MHz. This bandwidth difference was found to have a meaningful effect on SNR. The feasibility condition for the link budget was -6 dB SNR for uplink and 6 dB SNR for downlink. The link budget concluded that the first three bands (0.7, 1.9, and 2.7 GHz) are feasible with reasonably sized antennas. This meant a parabolic reflector dish diameter of 0.6 m for all three bands, or a phased array antenna with 4, 32, and 64 elements, respectively, that all resulted in a roughly equal physical size of the array. The 3.5 GHz frequency band was found to be feasible with a much larger antenna (512 element array). The bands above 3.5 GHz were not deemed feasible. The wide area investigation limited the antenna to a phased array antenna. Two cell layouts were considered for coverage: a 7 cell layout with one nadir cell surrounded by 6 cells and a 19 cell layout which encapsulates the former with another layer of 12 cells. The feasibility condition was that the half power beamwidth is equal to the angular size of a cell from the HIBS for each cell layer while maintaining gain. Beamwidth was controlled through array tapering and altering element configurations. This investigation concluded that coverage is feasible for two bands. In the 0.7 GHz band, the chosen option was a 7 cell layout using a single element antenna for the nadir cell and 3 by 1 arrays for the outer cells. In the 1.9 GHz band, the chosen option was a 19 cell layout with a single element antenna for the nadir cell, 5 by 1 arrays for the cells in the middle layer, and 8 by 5 arrays for the outer layer. Higher frequency bands required higher gain antennas which in turn did not provide adequate beamwidth for coverage.