Location and ages of terrestrial peatlands located on continental shelves and in coastal sediments

This synthesis dataset contains records of freshwater peat and lake sediments from continental shelves and coastal areas. Information included is site location (when available), thickness and description of terrestrial sediments as well as underlying and overlying sediments, dates (when available), and references.

Does worry affect adjustment to bereavement? A longitudinal investigation

This work was supported with a ZonMw TOP grant of the Dutch Association for Scientific Research (NWO), grant number: 91208009.

Cape Breton Provides Pointers for the Adjustment Programs Required by the Decline of the Old Economy (Working Paper 14)

The Cape Breton Development Corporation need not, should not, be dying in the way it is. “Devco” has been widely represented as a failure dragged out too long. In fact it had for a time considerable success in a role too difficult to be often attempted. The main failure was in the political will to stick to its purpose. This commentary is in three parts. The first, major section discusses the purpose of Devco and the policies that served the purpose quite well but were maintained for little more than a decade. It suggests that the benefits would have been greater if Devco had been started earlier in the period of postwar prosperity. The second section comments on the enfeeblement of Devco in the 1980s. The removal of its development function also weakened, and has eventually led to the abandonment of, Devco’s social purpose in the operation of coal mining. Third, a short epilogue pointing out that, while the Cape Breton case is extreme, there will be increasing need to moderate the socially disruptive consequences of accelerating economic change. There are lessons from the Devco experience: the longer remedial action is delayed, the more difficult and expensive it becomes, and the more necessary for its effectiveness is a steady political will.

Chemotherapy patient perception regarding communication with the healthcare staff

Purpose: To qualitatively explore the communication between healthcare professionals and oncology patients based on the perception of patients undergoing chemotherapy.Method: Qualitative and exploratory design. Participants were 14 adult patients undergoing chemotherapy at different stages of the disease. A socio-demographic and clinical data form was utilized along with semi-structured interviews. The interviews were audio-recorded, transcribed and content analysis was performed. Two independent judges evaluated the interview content in regards to emerging categories and obtained a Kappa index of 0.834.Results: Three categories emerged from the data: 1) Technical communication without emotional support, in which the information provided is composed of strictly technical information regarding the diagnosis, treatment and/or prognosis; 2) Technical communication, in which the information provided is oriented towards the technical aspects of the patient’s physical condition, while also providing psychological support for the patients’ subjective needs; and 3) Insufficient technical communication, win which there are gaps in the information provided causing confusion and suffering to the patient.Conclusions: Communication with emotional support contributes to greater satisfaction of chemotherapy patients. Practical implications: the results provide elements for the training of healthcare professionals regarding the importance of the emotional support that can be offered to cancer patients during their treatment.

UK consensus project on quality in palliative day services: developing a quality indicator set using the RAND/UCLA appropriateness method

Introduction
Evaluating quality of palliative day services is essential for assessing care across diverse settings, and for monitoring quality improvement approaches.

Aim
To develop a set of quality indicators for assessment of all aspects (structure, process and outcome) of care in palliative day services.

Methods
Using a modified version of the RAND/UCLA appropriateness method (Fitch et al., 2001), a multidisciplinary panel of 16 experts independently completed a survey rating the appropriateness of 182 potential quality indicators previously identified during a systematic evidence review. Panel members then attended a one day, face-to-face meeting where indicators were discussed and subsequently re-rated. Panel members were also asked to rate the feasibility and necessity of measuring each indicator.

Results
71 indicators classified as inappropriate during the survey were removed based on median appropriateness ratings and level of agreement. Following the panel discussions, a further 60 were removed based on appropriateness and feasibility ratings, level of agreement and assessment of necessity. Themes identified during the panel discussion and findings of the evidence review were used to translate the remaining 51 indicators into a final set of 27.

Conclusion
The final indicator set included information on rationale and supporting evidence, methods of assessment, risk adjustment, and recommended performance levels. Further implementation work will test the suitability of this ‘toolkit’ for measurement and benchmarking. The final indicator set provides the basis for standardised assessment of quality across services, including care delivered in community and primary care settings.

Reference

• Fitch K, Bernstein SJ, Aguilar MD, et al. The RAND/UCLA Appropriateness Method User’s Manual. Santa Monica, CA: RAND Corporation; 2001. http://www.rand.org/pubs/monograph_reports/MR1269

Date adjustment tools : Date rollover and Individual date adjustment

Learning Management Systems (LMSs) have become a larger part of teaching and learning in the modern world. Therefore has Moodle, a free and open source e-learning tool surfaced and gained a lot of attraction and downloads. A purpose of this study has been to develop a new local plugin in Moodle with guidelines from Magnus Eriksson and Tsedey Terefe. A purpose for this project has also been to build a plugin which has the functions Date rollover and Individual date adjustment. Mid Sweden University (Miun) stated that WebCT/Blackboard was in use before Moodle and some other LMSs and the dissatisfaction with WebCT/Blackboard was rife, however some teachers liked it. Therefore WebCT/Blackboard was abandoned and Moodle was embraced. The methods of gaining information has generally been web based sources and three interviews, likewise called user tests. Programs and other aids that have been used include but are not limited to: Google Drive, LTI Provider, Moodle, Moodle documentation, Notepad++, PHP and XAMPP. The plugin has been implemented as a local plugin. The result has shown that the coded plugin, Date adjustment tools could be improved and that it was changed. In the plugin, support for old American English dates were added and the code for using the two functions “Date rollover” and “Individual date adjustment” were rewritten to not interfere with one another. A conclusion to draw from the result is that the plugin has been improved from Terefe’s implementation, although more work can be made with the plugin Date adjustment tools.

Methods for Confounding Adjustment and High-Dimensional Environmental Exposures

Thesis (Ph.D.)--University of Washington, 2016-08

Assessing the resilience of a real-world social-ecological system: lessons from a multidisciplinary evaluation of a South African pastoral system

In the past decades, social-ecological systems (SESs) worldwide have undergone dramatic transformations with often detrimental consequences for livelihoods. Although resilience thinking offers promising conceptual frameworks to understand SES transformations, empirical resilience assessments of real-world SESs are still rare because SES complexity requires integrating knowledge, theories, and approaches from different disciplines. Taking up this challenge, we empirically assess the resilience of a South African pastoral SES to drought using various methods from natural and social sciences. In the ecological subsystem, we analyze rangelands’ ability to buffer drought effects on forage provision, using soil and vegetation indicators. In the social subsystem, we assess households’ and communities’ capacities to mitigate drought effects, applying agronomic and institutional indicators and benchmarking against practices and institutions in traditional pastoral SESs. Our results indicate that a decoupling of livelihoods from livestock-generated income was initiated by government interventions in the 1930s. In the post-apartheid phase, minimum-input strategies of herd management were adopted, leading to a recovery of rangeland vegetation due to unintentionally reduced stocking densities. Because current livelihood security is mainly based on external monetary resources (pensions, child grants, and disability grants), household resilience to drought is higher than in historical phases. Our study is one of the first to use a truly multidisciplinary resilience assessment. Conflicting results from partial assessments underline that measuring narrow indicator sets may impede a deeper understanding of SES transformations. The results also imply that the resilience of contemporary, open SESs cannot be explained by an inward-looking approach because essential connections and drivers at other scales have become relevant in the globalized world. Our study thus has helped to identify pitfalls in empirical resilience assessment and to improve the conceptualization of SES dynamics.

Preschoolers’ Executive Control and Diurnal Cortisol as Mediators of the Relations of Prenatal Stress to Child Adjustment

Thesis (Ph.D.)--University of Washington, 2016-08

Indirect Effects of Parental Coping on Children’s Psychosocial Adjustment in the Context of Pediatric Cancer

Thesis (Ph.D.)--University of Washington, 2016-08

Understanding sound art practice: a multidisciplinary approach

Sound art has a fragmented scholarship struggling to find appropriate terminology to understand and explain itself. In this context the practitioner’s perspective is often marginalised. This thesis seeks to develop new perspectives on contemporary sound practice, informed by a multi-disciplinary approach to auditory scholarship and interviews with Australian sound practitioners. The model that emerges describes an ecology of contemporary sound art where practitioners continually negotiate disciplinary and institutional boundaries while articulating distinctive models of listening and conceptions of sound. This thesis proposes a theoretical approach to sound art that recognises its interdisciplinarity as well as a distinctive engagement with the embodied experience of sound and listening.

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

Changing use and hydromorphological adjustment in a coastal lagoon–estuarine system, the Ria de Aveiro, Portugal

Today the Ria de Aveiro of northern Portugal has a hydromorphological regime in which river influence is limited to periods of flood. For most of the annual cycle, tidal currents and wind waves are the major forcing agents in this complex coastal lagoon–estuarine system. The system has evolved over two centuries from one that was naturally fluvially dominant to one that is today tidally dominant. Human influence was a trigger for these changes, starting in 1808 when its natural evolution was halted by the construction of a new inlet/outlet channel through the mobile sand spit that isolates it from the Atlantic Ocean. In consequence, tidal ranges in the lagoon increased rapidly from ~0.1 m to >1 m and continued to increase, as a result of continued engineering works and dredging, today reaching ~3 m on spring tides. Hydromorphological adjustments that have taken place include the deepening of channels, an increase in the area of inter-tidal flats, regression of salt marsh, increased tidal propagation and increased saline intrusion. Loss of once abundant submerged aquatic vegetation (SAV), due to increased tidal flows, exacerbated by increased recreational activities, has been accompanied by a change from fine cohesive sediments to coarser, mobile sediments with reduced biological activity.

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.