Complexity and Conundrums in Pollution Control:Improving Effectiveness in Regulatory Compliance

The last century has witnessed a dramatic increase in the wealth of European nations and the well being of their inhabitants. The focus has, however, largely been upon economic growth to the detriment of people and the environment. It is only in recent years that governments have taken cognisance of the impacts of our actions and there is a growing realisation that the causal factors must be identified and addressed as a matter of urgency. One of the key problem areas is pollution and as such environmental protection has become increasingly important as a mechanism for safeguarding the quality of air, water and land. This involves a range of activities from setting standards to monitoring and reporting on discharges and emissions, through to the enforcement of legislation. In theory, this is a simple challenge, in practice, it has proven to be an extremely complex equation that might only begin to be addressed through research. In this context it is strange, and alarming, to find that while it is an axiom of good practice that policy is informed by research there has been a dearth of investigation in this field. The purpose of this paper is, therefore, to consider the issue of pollution, how it impacts on the environment, what measures have been established in pursuit of reducing the number of incidences and, most significantly, which strategies might be employed to avoid or ameliorate detrimental impacts.

The determinants of performance appraisal systems: A note (Do Brown and Heywood's results for Australia hold up for Britain?)

This article offers a replication for Britain of Brown and Heywood's analysis of the determinants of performance appraisal in Australia. Although there are some important limiting differences between our two datasets - the Australia Workplace Industrial Relations Survey (AWIRS) and the Workplace Employment Relations Survey (WERS) - we reach one central point of agreement and one intriguing shared insight. First, performance appraisal is negatively associated with tenure: where employers cannot rely on the carrot of deferred pay or the stick of dismissal to motivate workers, they will tend to rely more on monitoring, ceteris paribus. Second, employer monitoring and performance pay may be complementary. However, consonant with the disparate results from the wider literature, there is more modest agreement on the contribution of specific human resource management practices, and still less on the role of job control.

Testing a social ecological model for relations between political violence and child adjustment in Northern Ireland

Relations between political violence and child adjustment are matters of international concern. Past research demonstrates the significance of community, family, and child psychological processes in child adjustment, supporting study of interrelations between multiple social ecological factors and child adjustment in contexts of political violence. Testing a social ecological model, 300 mothers and their children (M = 12.28 years, SD = 1.77) from Catholic and Protestant working class neighborhoods in Belfast, Northern Ireland, completed measures or community discord, family relations, and children's regulatory processes (i.e., emotional security) and outcomes. Historical political violence in neighborhoods based on objective records (i.e., politically motivated deaths) were related to family members' reports of current sectarian antisocial behavior and nonsectarian antisocial behavior. Interparental conflict and parental monitoring and children's emotional security about both the community and family contributed to explanatory pathways for relations between sectarian antisocial behavior in communities and children's adjustment problems. The discussion evaluates support for social ecological models for relations between political violence and child adjustment and its implications for understanding relations in other parts of the world.

Increasing the accuracy of Nitrogen Dioxide (NO2) pollution mapping using geographically weighted regression (GWR) and geostatistics.

Nitrogen Dioxide (NO2) is known to act as an environmental trigger for many respiratory illnesses. As a pollutant it is difficult to map accurately, as concentrations can vary greatly over small distances. In this study three geostatistical techniques were compared, producing maps of NO2 concentrations in the United Kingdom (UK). The primary data source for each technique was NO2 point data, generated from background automatic monitoring and background diffusion tubes, which are analysed by different laboratories on behalf of local councils and authorities in the UK. The techniques used were simple kriging (SK), ordinary kriging (OK) and simple kriging with a locally varying mean (SKlm). SK and OK make use of the primary variable only. SKlm differs in that it utilises additional data to inform prediction, and hence potentially reduces uncertainty. The secondary data source was Oxides of Nitrogen (NOx) derived from dispersion modelling outputs, at 1km x 1km resolution for the UK. These data were used to define the locally varying mean in SKlm, using two regression approaches: (i) global regression (GR) and (ii) geographically weighted regression (GWR). Based upon summary statistics and cross-validation prediction errors, SKlm using GWR derived local means produced the most accurate predictions. Therefore, using GWR to inform SKlm was beneficial in this study.

Dynamic grey-box modeling for online monitoring of extrusion viscosity

Melt viscosity is a key indicator of product quality in polymer extrusion processes. However, real time monitoring and control of viscosity is difficult to achieve. In this article, a novel “soft sensor” approach based on dynamic gray-box modeling is proposed. The soft sensor involves a nonlinear finite impulse response model with adaptable linear parameters for real-time prediction of the melt viscosity based on the process inputs; the model output is then used as an input of a model with a simple-fixed structure to predict the barrel pressure which can be measured online. Finally, the predicted pressure is compared to the measured value and the corresponding error is used as a feedback signal to correct the viscosity estimate. This novel feedback structure enables the online adaptability of the viscosity model in response to modeling errors and disturbances, hence producing a reliable viscosity estimate. The experimental results on different material/die/extruder confirm the effectiveness of the proposed “soft sensor” method based on dynamic gray-box modeling for real-time monitoring and control of polymer extrusion processes. POLYM. ENG. SCI., 2012. © 2012 Society of Plastics Engineers

Mapping stone surface temperature fluctuations: Implications for lichen distribution and biomodification on historic stone surfaces

The exposure of historic stone to processes of lichen-induced surface biomodification is determined, first and foremost, by the bioreceptivity of those surfaces to lichen colonization. As an important component of surface bioreceptivity, spatiotemporal variation in stone surface temperature plays a critical role in the spatial distribution of saxicolous lichen on historic stone structures, especially within seasonally hot environments. The ornate limestone and tufa stairwell of the Monastery of Cartuja (1516), Granada, Spain, exhibits significant aspect-related differences in lichen distribution. Lichen coverage and
diurnal fluctuations in stone surface temperature on the stairwell were monitored and mapped, under anticyclonic conditions in summer and winter, using an infrared thermometer and Geographical Information Systems approach. This research suggests that it is not extreme high surface temperatures that
determine the presence or absence of lichen coverage on stonework. Instead, average stone surface temperatures
over the course of the year seem to play a critical role in determining whether or not surfaces are receptive to lichen colonization and subsequent biomodification. It is inferred that lichen, capable of surviving extreme surface temperatures during the Mediterranean summer in an ametabolic state, require a respite period of lower temperatures within which they can metabolize, grow and reproduce.
The higher the average annual temperature a surface experiences, the shorter the respite period for any lichen potentially inhabiting that surface. A critical average temperature threshold of approximately 21 ?C has been identified on the stairwell, with average stone surface temperatures greater than this
generally inhibiting lichen colonization. A brief visual condition assessment between lichen-covered and lichen-free surfaces on the limestone sections of the stairwell suggests relative bioprotection induced by lichen coverage, with stonework quality and sharpness remaining more defined beneath lichen-covered surfaces. The methodology employed in this paper may have further applications in the monitoring and mapping of thermal stress fatigue on historic building materials.

Distributed Adaptive Learning Framework for Wide Area Monitoring of Power Systems Integrated with Distributed Generations

This paper presents a preliminary study of developing a novel distributed adaptive real-time learning framework for wide area monitoring of power systems integrated with distributed generations using synchrophasor technology. The framework comprises distributed agents (synchrophasors) for autonomous local condition monitoring and fault detection, and a central unit for generating global view for situation awareness and decision making. Key technologies that can be integrated into this hierarchical distributed learning scheme are discussed to enable real-time information extraction and knowledge discovery for decision making, without explicitly accumulating and storing all raw data by the central unit. Based on this, the configuration of a wide area monitoring system of power systems using synchrophasor technology, and the functionalities for locally installed open-phasor-measurement-units (OpenPMUs) and a central unit are presented. Initial results on anti-islanding protection using the proposed approach are given to illustrate the effectiveness.

Psychological responses after a major fatal earthquake: The effect of preitraumatic dissociation and posttraumatic stress symptoms on anxiety and depression.

Following trauma, most people with initial symptoms of stress recover, but it is important to identify those at risk for continuing difficulties so resources are allocated appropriately. There has been limited investigation of predictors of posttraumatic stress disorder following natural disasters. This study assessed psychological difficulties experienced in 101 adult treatment seekers following exposure to a significant earthquake. Peritraumatic dissociation, posttraumatic stress symptoms, anxiety, depression, and emotional support were assessed. Path analysis was used to determine whether the experience of some psychological difficulties predicted the experience of other difficulties. As hypothesized, peritraumatic dissociation was found to predict posttraumatic stress symptoms and anxiety. Posttraumatic stress symptoms then predicted anxiety and depression. Depression and anxiety were highly correlated. Contrary to expectations, emotional support was not significantly related to other psychological variables. These findings justify the provision of psychological support following a natural disaster and suggest the benefit of assessing peritraumatic dissociation and posttraumatic stress symptoms soon after the event to identify people in need of monitoring and intervention.

Microneedle-Mediated Minimally Invasive Patient Monitoring

Background: The emerging field of microneedle-based minimally invasive patient monitoring and diagnosis is reviewed. Microneedle arrays consist of rows of micron-scale projections attached to a solid support. They have been widely investigated for transdermal drug and vaccine delivery applications since the late 1990s. However, researchers and clinicians have recently realized the great potential of microneedles for extraction of skin interstitial fluid and, less commonly, blood, for enhanced monitoring of patient health.

Methods: We reviewed the journal and patent literature, and summarized the findings and provided technical insights and critical analysis.

Results: We describe the basic concepts in detail and extensively review the work performed to date.

Conclusions: It is our view that microneedles will have an important role to play in clinical management of patients and will ultimately improve therapeutic outcomes for people worldwide.

Low-cost Process monitoring for polymer extrusion

Polymer extrusion is regarded as an energy-intensive production process, and the real-time monitoring of both energy consumption and melt quality has become necessary to meet new carbon regulations and survive in the highly competitive plastics market. The use of a power meter is a simple and easy way to monitor energy, but the cost can sometimes be high. On the other hand, viscosity is regarded as one of the key indicators of melt quality in the polymer extrusion process. Unfortunately, viscosity cannot be measured directly using current sensory technology. The employment of on-line, in-line or off-line rheometers is sometimes useful, but these instruments either involve signal delay or cause flow restrictions to the extrusion process, which is obviously not suitable for real-time monitoring and control in practice. In this paper, simple and accurate real-time energy monitoring methods are developed. This is achieved by looking inside the controller, and using control variables to calculate the power consumption. For viscosity monitoring, a ‘soft-sensor’ approach based on an RBF neural network model is developed. The model is obtained through a two-stage selection and differential evolution, enabling compact and accurate solutions for viscosity monitoring. The proposed monitoring methods were tested and validated on a Killion KTS-100 extruder, and the experimental results show high accuracy compared with traditional monitoring approaches.

Promoting Dignity for All: Human Rights Approaches in the Post-2015 Climate Change, Disaster Risk Reduction and Sustainable Development Frameworks

This article takes as its starting point the potentially negative human rights implications that the effects of climate change, disasters and development practices can have on individuals and communities. It argues that key international instruments, including the post-2015 successors to the Kyoto Protocol, Hyogo Framework for Action on disaster risk reduction and the Millennium Development Goals, appear to be moving towards an express acknowledgment of the relevance of international human rights law as an important mechanism to minimise potential harms that may arise. This raises the question as to the appropriate role of the UN human rights monitoring and accountability mechanisms in identifying the relevant rights-holders and duty-bearers. The article therefore provides an examination of the linkages between climate change and international human rights law, as well as discussion of the human rights considerations and accountability mechanisms for disasters and sustainable development. The article concludes by arguing that despite differential understandings between disciplines as to the meaning of key terms such as ‘vulnerability’ and ‘resilience’, international human rights law provides a comprehensive basis for promoting international and national accountability. It follows that a greater level of coordination and coherence between the human rights approaches of the various post-2015 legal and policy frameworks is warranted as a means of promoting the dignity of those most affected by climate change, disasters and developmental activities.

MSC-clustering and forward stepwise regression for virtual metrology in highly correlated input spaces

Increasingly semiconductor manufacturers are exploring opportunities for virtual metrology (VM) enabled process monitoring and control as a means of reducing non-value added metrology and achieving ever more demanding wafer fabrication tolerances. However, developing robust, reliable and interpretable VM models can be very challenging due to the highly correlated input space often associated with the underpinning data sets. A particularly pertinent example is etch rate prediction of plasma etch processes from multichannel optical emission spectroscopy data. This paper proposes a novel input-clustering based forward stepwise regression methodology for VM model building in such highly correlated input spaces. Max Separation Clustering (MSC) is employed as a pre-processing step to identify a reduced srt of well-conditioned, representative variables that can then be used as inputs to state-of-the-art model building techniques such as Forward Selection Regression (FSR), Ridge regression, LASSO and Forward Selection Ridge Regression (FCRR). The methodology is validated on a benchmark semiconductor plasma etch dataset and the results obtained are compared with those achieved when the state-of-art approaches are applied directly to the data without the MSC pre-processing step. Significant performance improvements are observed when MSC is combined with FSR (13%) and FSRR (8.5%), but not with Ridge Regression (-1%) or LASSO (-32%). The optimal VM results are obtained using the MSC-FSR and MSC-FSRR generated models. © 2012 IEEE.

Choosing and using diversity indices: Insights for ecological applications from the German Biodiversity Exploratories

Biodiversity, a multidimensional property of natural systems, is difficult to quantify partly because of the multitude of indices proposed for this purpose. Indices aim to describe general properties of communities that allow us to compare different regions, taxa, and trophic levels. Therefore, they are of fundamental importance for environmental monitoring and conservation, although there is no consensus about which indices are more appropriate and informative. We tested several common diversity indices in a range of simple to complex statistical analyses in order to determine whether some were better suited for certain analyses than others. We used data collected around the focal plant Plantago lanceolata on 60 temperate grassland plots embedded in an agricultural landscape to explore relationships between the common diversity indices of species richness (S), Shannon's diversity (H'), Simpson's diversity (D1), Simpson's dominance (D2), Simpson's evenness (E), and Berger–Parker dominance (BP). We calculated each of these indices for herbaceous plants, arbuscular mycorrhizal fungi, aboveground arthropods, belowground insect larvae, and P. lanceolata molecular and chemical diversity. Including these trait-based measures of diversity allowed us to test whether or not they behaved similarly to the better studied species diversity. We used path analysis to determine whether compound indices detected more relationships between diversities of different organisms and traits than more basic indices. In the path models, more paths were significant when using H', even though all models except that with E were equally reliable. This demonstrates that while common diversity indices may appear interchangeable in simple analyses, when considering complex interactions, the choice of index can profoundly alter the interpretation of results. Data mining in order to identify the index producing the most significant results should be avoided, but simultaneously considering analyses using multiple indices can provide greater insight into the interactions in a system.