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e-ISSN: 2348-6848 p-ISSN: 2348-795X Volume 05 Issue 22 November 2018

How to increase public engagement in environmental issues is a central question in environmental communication and environmental psychology literatures. Psychological distance (PD) is one potential barrier to public engagement. PD describes the perceptual distance between a person’s experience and an external target. Immersive technologies such as virtual reality (VR) have the potential to shrink PD. Yet, we know little about how people perceive and react to an environmental VR experience or about the role that PD plays in these reactions. To address this gap, we use interviews and focus groups to examine how people react to a VR experience about ocean acidification. We find that PD is prevalent in the reactions of VR users, especially first-time users, and that 62% of participants described feeling psychologically close to ocean acidification.

Urban and suburban lawns make up a large share of land use in the US. Maintaining lawns to fulfill aesthetic norms has environmental consequences. In this analysis, we examine household decisions to apply nitrogen-containing lawn fertilizer. Using survey data of 298 households in Nashville, Tennessee, we first examine the prevalence of fertilizer use and the rate of annual nitrogen applied. We find that the resulting distribution is skewed, with the top 20% of the sample applying 56% of the total share of nitrogen. In contrast to this subset of “intensive” fertilizers, 93% of households applied at or below levels recommended by landscaping professionals, challenging the assumption that the over-application of fertilizer is widespread. We employed multi-level modeling to examine the relative importance of household- and block-level characteristics on fertilizer use and the intensity of use. Consistent with prior work, we find that the desire for a green lawn is a significant predictor of fertilizer use. However, we also find that living on a wealthy block and living near others who value a green lawn independently predict fertilizer use. In addition, we observe that intensive fertilizing households tend to be less wealthy than others on their block, suggesting the possibility of an aspirational dimension to fertilizer use. Finally, we find evidence that environmental concern is associated with less intensive fertilizer use, suggesting that households may be willing to take some steps to mitigate the impact of their lawn care on the environment.

This paper provides orthogonal moments (OM) such as, Zernike Moments(ZM), Psuedo Zernike Moments(PZM) and Orthogonal Fourier Mellin Moments(OFMM) for the analysis of melanoma images. The moment invariants may vary with respect to geometric variations. For the analysis of orthogonal moments hundred random melanoma images and hundred non-melanoma images have been taken into consideration from the database of 570 melanoma images and 250 non-melanoma images respectively. Orthoganal moments have been computed by varying the phase angles from 10° to 40° with an equal interval of 10° degree for the orders 2, 4,8,16,32,64,128,256 respectively. For the optimal OMs Particle Swarm Optimization (PSO) technique have been used. These set of extracted optimal OMs have been further applied to classify melanoma images. Support Vector Machine (SVM) has been used for the classification of [1]sensitivity=88.78%.

The quality of combustion process has an impact on combustion itself and mainly on emissions. This1 latter is one of the major concerns in an environmental viewpoint; for instance, the amount of oxygen is an indicator of bad and good combustion. It is also a constraint for regulating pollutant production, in particular dust that is also a vector transporting harmful micropollutants. The paper illustrates combustion quality detection by means of imaging. The work aims at retrieving possible precursors of combustion deterioration, and instability and allowing decision makers to provide accordingly. Images have been taken from an experimental setup.

With the wide spread use of wearable technology, particularly smart watches equipped with multiple sensors, we find that smart watches are mainly geared towards fitness advocates in order to help individuals manage and track their respective athletic performance. What is less understood is the under-utilized data analytics potential of wearable technology. Most smart watches presently are making heart rate sensors readily available and commercially affordable. In this paper we explore how we can better utilize smart watch data analytics to further improve an individual's awareness of their cardiac health condition and use this data to potentially facilitate preventative behaviors, with the ultimate goal of this leading to the earlier detection of cardiac diseases.

Looming concerns on rising food prices and food security has slowed down the impetus in biofuel production. The development of the sub-sector, however, remains an important agenda among developing countries like those of the Greater Mekong Sub-region (GMS) that have abundant labour and natural resources but have limited supply of fossil fuels which continues to serve as a constraint to economic growth. Five crops have been selected to be further developed and used for biofuel production in the GMS, namely sugarcane, cassava, oil palm, sweet sorghum and Jathropa curcas . The expanded use of sugarcane, cassava, and oil palm for biofuel production can cause problems in the food sector. The other two crops, sweet sorghum and J. curcas , are non-food crops but could still compete with the food crops in terms of resource use for production. In all cases, the GMS needs to formulate a sustainable strategy for the biofuel development that will not compete with the food sector but will rather help achieve energy security, promote rural development and protect the environment. Except for People’s Republic of China (PRC) and Thailand that already have fairly developed biofuel sub-sector, the other GMS countries are either poised to start ((the) Lao People’s Democratic Republic and Cambodia) or ready to enhance existing initiatives on biofuel production (Myanmar and Viet Nam), with support from their respective governments. Biofuel development in these countries has to be strongly integrated with smallholder producers in order to have an impact on improving livelihood. At this initial stage, the sub-sector does not need to compete on a price basis but should rather aim to put up small-scale biofuel processing plants in remote rural areas that can offer an alternative to high-priced diesel and kerosene for local electricity grids serving homes and small enterprises. The social and economic multiplier effects are expected to be high when farmers that produce the energy crops also produce the biofuels to generate affordable and reliable energy. To make this happen, there is a need for conscious effort and investment support from development agencies and the government working in partnership with the private sector, research institutions and the farmers.

Since the late 1980s wildfire activity and suppression costs have been increasing dramatically in the United States. Every year the US Forest Service spends hundreds of millions, or billions, of dollars fighting fires, regularly depleting its suppression budget, and requiring supplemental funds to pay for firefighting expenditures. Excess fuel accumulations, an expanding wildland-urban interface, and climate change are the most common reasons blamed for rising suppression costs, but the “human dimensions” affecting fire management are key factors driving aggressive suppression strategies that are more expensive and ecologically damaging than alternative fire use strategies and tactics. We present the most recent suppression cost data and review the literature discussing some of the sociocultural, political, institutional, and operational factors that influence suppression costs. We provide recommendations for fixing the US Forest Service suppression budget, changing managerial incentives, and adopting “ecological fire use” as a new fire management philosophy and strategy for controlling costs, protecting communities, and restoring fire-dependent ecosystems.

Many industrial and transportation applications use combustion in dedicated chambers. Combustion implies, depending upon the nature and the amount of precursors, production of carbon dioxide, pollutants and dusts in terms of particulate matters. With the aim of reducing emission, lean combustion is of great interest. However flame stability within the combustor chamber is a key issue under lean conditions. In fact under lean conditions burners exhibit flame instability, flashback or lean blow out, until the flame extinction. Hence the online monitoring of these phenomena related to combustion instability is essential. One the most used techniques is to check temperature and flame stability by means of sensing probes resisting to high temperatures. Increasing the number of probes, it is possible to perform a 2D and 3D monitoring. However since these probes are costly and require heavy maintenance procedures, it could be wise to exploit imaging processing through cameras directed to portholes across which we can see inner parts, and atmosphere of the furnace/chamber. This paper illustrates findings related to monitoring the flame behaviour different operating conditions chamber by an advanced image processing. A specific algorithm has been developed to characterize the flame, hence, to perform measurements. Myriad filters have been utilized to enhance flame features.

In this paper, an overview of the numerous biomedical applications of Carbon-Based Nano-Material's (CBNMs) is highlighted. The script is initiated by describing the various CBNMs that are the most important for biomedical applications. Secondly, critical survey on research efforts made by different researchers in the development of biomedical applications has been focused. The inclusion are; drug/gene delivery, bio-imaging, biosensors, and therapeutics. For each of the case, hypothetical general information and a brief explanation has been set. Lastly, important constraints associated with carbon-based nonmaterial are touched upon and possible open issues are debated for further research.

Non-Linear Polynomial Filters (NPF) consists of a framework of weighted coefficients of low-pass and high pass filters. This paper explores the applicability of NPF for the contrast enhancement of breast tumors in mammograms. NPF algorithm in the present work has been improved to provide controlled background suppression during the mammogram enhancement. This is because, in the process to control overshoots and visualization of tumor margins; the uncontrolled background suppression may lead to loss of finer details in the vicinity of the lesion region. Simulation results have shown that the response of the proposed NPF has been reasonably good on mammograms containing tumors embedded in different types of background tissues.

The scope of this Special Issue is to present and highlight the advances and the latest novel and emergent technologies, implementations and applications in the field of smart transducers and sensors according to the guidelines of the family of standards. Therefore, it provides an overview of the current standards features and the related state of the art. The final aim of the Special Issue is to stimulate the international scientific community and industry to suggest new features and ideas addressing the design and development of smart transducers and sensors.

Electroencephalogram (EEG) remains the most immediate, simple, and rich source of information for understanding phenomena related to brain electrical activities. It is certainly a source of basic and interesting information to be extracted using specific and appropriate techniques. The most important aspect in processing EEG signals is to use less co-lateral assets and instrumentation in order to carried out a possible diagnosis; this is the approach of early diagnosis. Advanced estimate spectral analysis can reveal new information encompassed in EEG signals by means of specific parameters or indices. The research proposes a multidimensional approach with a combined use of decimated signal diagonalization (DSD) as basis from which it is possible to work by finding appropriate signal windows for revealing expected information and overcoming signal processing limitations encountered in quantitative EEG. Important information, about the state of the patient under observation, must be extracted from calculated DSD bispectrum. For this aim, it is useful to define an assessment index about the dynamic process associated with the analyzed signal. This information is measured by means of entropy, since the degree of order/disorder of the recorded EEG signal will be reflected in the obtained DSD bispectrum. The general advantage of multidimensional approach is to reveal eventual stealth frequencies “in space and in time” giving a topological vision to be correlated to physical areas which these frequencies emerge from. Long term and sleeping EEG recorded are analyzed, and the results obtained are of interest for an accurate diagnosis of the patient's clinical condition.

This paper presents a preliminary analysis of a class of non-linear filters for enhancement of mammogram lesions. A non-linear filtering approach employing polynomial model of non-linearity is designed by second order truncation of Volterra series expansion. The proposed filter response is a linear combination of Type-0 and Type-II Volterra filters. Type-0 filter provides contrast enhancement, suppressing the ill-effects of background noise. On the other hand, Type-II filter employs edge enhancement. The objective analysis of the proposed filter is carried out by estimating values of quality parameters like CEM and PSNR on mammograms from MIAS and DDSM databases.

Computer aided detection of mammographic masses can be improved to a greater extent employing non-linear filters for image enhancement. The present work proposes a truncated Volterra filter combination to provide contrast enhancement as well as texture based processing of masses in digital mammograms. Noteworthy improvement in visualization of masses has been observed in the simulation results carried out on cases from DDSM database. The improved performance of the proposed filtering approach is well supported with calculated values of objective evaluation parameters.

Epilepsy is one of the most common neurological disorders. It is very difficult the identification of seizures because they are infrequent events to be detected and an electroencephalogram (EEG) does not always show abnormalities due to interferences and noise. In this work, methods based on principles of spatial filtering are developed to allow a more accurate diagnosis. For this aim techniques based on Beamforming are taken into account for their ability to constrain the passing of the electrical activity from a specified location attenuating the activity to others. The problem is set as a waveform estimation, so information about amplitude and frequency of the signal are extracted from measured EEG signal. The reconstruction of the desired signal, that is the possible signal originated from epileptogenic focus, will highlight abnormalities or not in the electrical activity of the brain, and will help to have accurate diagnosis. For this scope, in this work, EEG data by scalp are simulated, in a preliminary way, through the solution of a forward problem related to geometry of the head and to the definition of a model for the envisaged source. A simple geometry is considered and, simulations and results are presented, showing the performance of several algorithms. Following the methods have been applied on real data, In this case it is necessary to know the position of the source for to reconstructing of the signal of interest. As per alternative, to estimate the position of the sources, methods of a DOA estimation have been studied. In a preliminary way this problem is faced with consideration on the resolution issue of the sources when they are very close together.