Do transportation taxes promote pro-environmental behaviour? An empirical investigation.

The transportation sector is a crucial driver of energy intensity and environmental degradation. Therefore, we aim to explore the nexus of transportation taxes, energy intensity, and CO2 emissions for the BICS economies. The econometric approaches, CS-ARDL and PMG-ARDL, have been employed to compute the estimates. The long-run estimates of the green transportation tax variable are negatively significant in both energy intensity and CO2 emissions models irrespective of the estimation technique. These findings imply that green transportation taxes help reduce energy intensity and CO2 emissions in BICS economies. Conversely, in the short-run, the effects of transportation taxes on energy intensity and CO2 emissions are mixed and inconclusive. Hence, transportation taxes are necessary to keep the polluters under control not only from the transport sector but also serve as a deterrent for other sectors as well.

Epidemiological and hematological investigation of dengue virus infection.

Dengue is a mosquito-borne viral illness that infects humans. For the past few decades, it has been declared a global public health problem. The current study, conducted at the district headquarter hospital (DHQ) Bannu between June to September 2018, was based on the seroprevalence of antibodies against dengue virus serotypes and their hematological parameters among the patients. A total of 1738 individuals suspected of having dengue were diagnosed through NS1, IgG, and IgM antibodies and RT-PCR techniques. Of all the samples, 716 (41.19%) were found to be positive for dengue. A higher infection rate was found in males (65.92%) compared with females (34.07%). The most affected age group was 16-40 years, whereas the most affected tehsil was Bannu, where the DENV-3 serotype was prevalent. The rare serotype (DENV-4) was found in 1% of cases. Symptoms including fever (100%), myalgia (100%), headache (61.31%), vomiting (34.63%), and rashes were common among the dengue patients. However, the mild cases showed fewer clinical signs compared with the severely infected cases. The study also revealed a significant association (P < 0.05) between hematological parameters and dengue infection, showing a significant decrease in TC, eosinophils, neutrophils, and platelets and a significant increase in monocytes and lymphocytes. Based on the current report, it is concluded that patients with the above symptoms and hematological changes may have an increased probability of dengue and should be kept under observation to separate dengue-positive patients and to enhance the treatment process.

Optic disc localization in fundus images through accumulated directional and radial blur analysis.

Optic disc localization, a key preprocessing step in the analysis of color fundus images for diagnoses of eye diseases and the localization of various anatomical structures, is particularly challenging when input retina images contain abnormalities. In such cases, the disc can be confused with other anatomical structures such as fovea, exudates, vessel tree extraction, and retinopathy-related lesions. Herein, we present a method for effective optic disc detection and localization based on color and blur analysis. In this method, the input color fundus image is converted to CIE L*a*b* color space to enhance optic disc appearance and contrast, and the accumulated directional blur and extended-maxima transform are then applied to precisely extract optic disc candidates. Subsequently, radial blur is applied to each candidate to obtain better profiles and thus distinguish the optic disc from other candidates. Finally, the full width at 80% maximum (FW80M) metric is used to select the optic disc. The performance of the proposed method is evaluated using well-studied data sets, and comparison of the obtained results with those of state-of-the-art techniques reveals the effectiveness of our method and shows that it can precisely locate the disc position not only in normal cases but also in the presence of exudates and abnormalities.

The relevance of economic freedom for energy, environment, and economic growth in Asia-Pacific region.

This paper investigates the relationship between energy intensity, economic freedom, and carbon emissions. The problem of environmental degradation, economic freedom, and energy use is mainly studied for developed economies; however, this study has selected forty-one Asia-Pacific economies representing all income groups of the World Bank's classification. In the presence of income, economic freedom plays a dual role for environment and energy: direct impact and as moderating factor impact. Here, we empirically test for a panel of 41 Asia-Pacific countries using the autoregressive distributed lag approach. Our findings suggest, although there is no bidirectional causality between all the variables, the long-run estimates of economic freedom for economy and environment are positive. The results imply for substantial structural reforms with a favorable economic and regulatory environment for Asia-Pacific countries. Our empirical analysis also implies that GDP growth levels for Asia-Pacific countries are becoming increasingly dependent on economic freedom and energy intensity. The results underline the critical role played directly and indirectly by economic freedom in creating an atmosphere that promotes research and development activities to help reduce energy intensity shortly to solve environmental problems.

Robust Focus Volume Regularization in Shape From Focus.

Shape from focus (SFF) reconstructs 3D shape of the scene from a sequence of multi-focus images, and the quality of reconstructed shape mainly depends on the accuracy of image focus volume (FV). Traditional SFF techniques exhibit poor performance in preserving structural edges and fine details while removing noisy artifacts, and mostly they do not incorporate any additional shape prior. Therefore, in this paper, we propose to refine FV by formulating an energy minimization framework that employs a nonconvex regularizer and incorporates two types of shape priors. The proposed regularizer is robust against noisy focus values. The first proposed shape prior is input image sequence and it is a single and static shape prior. While, the second shape prior corresponds to a series of shape priors. These shape priors are FVs which are iteratively obtained on-the-fly. Both of these shape priors constrain the solution space for output FV. We optimize nonconvex energy function through majorize-minimization algorithm which iteratively guarantees a local minimum and converges quickly. Experiments have been conducted to evaluate accuracy and convergence properties of the proposed method. Experimental results of synthetic and real image sequences demonstrate that our method achieves superior results in terms of ability to reconstruct accurate 3D shapes as compared to existing approaches.

Depth Hypotheses Fusion through 3D Weighted Least Squares in Shape from Focus.

In shape-from-focus (SFF) methods, a single focus measure is used to compute the focus volume. However, it seems that a single focus measure operator is not capable of computing accurate focus values for the images of diverse types of object shapes. Furthermore, most of the SFF methods try to improve the depth map without considering any additional structural or prior information. Consequently, the extracted shape of the object might lack important details. In this work, we address these problems and suggest a method in which depth hypotheses are combined for a more accurate 3D shape through 3D weighted least squares. First, depth hypotheses are obtained by applying a number of focus operators. Then, structural prior or guidance volume is extracted from the focus measure volumes. Finally, a 3D weighted least squares optimization technique is applied to the depth hypothesis volume, where weights are computed from the guidance volume. Thus, by inducing structural prior, an improved resultant depth map is obtained. The proposed method was tested using various image sequences of synthetic and microscopic real objects. Experimental results and comparative analysis demonstrated the effectiveness of the proposed method.

Depth enhancement through correlation-based guided filtering in shape from focus.

Mostly, shape-from-focus (SFF) techniques do not consider any guidance or prior information from the input image sequence while improving the depth information. Consequently, the resultant depth maps may have inaccuracies due to missing the fine structural details. In this paper, we propose a depth enhancement method based on a novel guidance map and guided filtering. In the proposed method, first, a focus measure is applied on image sequence (volume) to compute the focus volume and an initial depth map is obtained by maximizing the focus measure in the optical direction. The guidance map is computed based on the correlation among the image volume and the focus volume along the optical axis. Finally, the improved depth map is obtained by applying guided filtering of initial depth by incorporating the weights from the suggested guidance map. Experiments were carried out using synthetic and real and microscopic image sequences of various objects. Experimental results have demonstrated the effectiveness of the proposed method and a reasonable improvement has been achieved in the quality of reconstructed depth maps.

Energy capacity, industrial production, and the environment: an empirical analysis from Pakistan.

This paper aims to find out relationships among the energy, environment, and the industrial production for a developing country which is in earlier stages of development. It also tests a few contradicting hypotheses to find the possible shape of an environmental Kuznets curve. Using the time series data, the study finds robust long-run relationships between energy, environment, and industrial production for Pakistan. The scale economy is also assumed. It is also found that the capital and labor elasticities of income show increasing returns in the presence of energy and emission variables. It finds evidence of EKC in a quadratic restricted model but not in a cubic function. This analysis implies that the focus of policy authorities should be to persuade environment-friendly energy resources. After an initial stage of economic development, society has to take serious measure to tackle the issues of environmental degradation.

Diabetic retinopathy detection through novel tetragonal local octa patterns and extreme learning machines.

Diabetic retinopathy (DR) is an eye disease that victimize the people suffering from diabetes from many years. The severe form of DR results in form of the blindness that can initially be controlled by the DR-screening oriented treatment. The effective screening programs require the trained human resource that manually grade the fundus images to understand the severity of the disease. But due to the complexity of this process, and the insufficient number of the trained workers, the precise manual grading is an expensive process. The CAD-based solutions try to address these limitations but most of the existing DR detection systems are as evaluated over small sets and become ineffective when applied in real scenarios. Therefore, in this paper we proposed a novel technique to precisely detect the various stages of the DR by extending the research of the content-based image retrieval domain. To achieve the human-level performance over the large-scale DR-datasets (i.e. Kaggle-DR), the fundus images are represented by the novel tetragonal local octa pattern (T-LOP) features, that are then classified through the extreme learning machine (ELM). To justify the significance of the method, the proposed scheme is compared against several state-of-the-art methods including the deep learning-based methods over four DR-datasets of variational lengths (i.e. Kaggle-DR, DRIVE, Review-DB, STARE). The experimental results confirm the significance of the DR-detection scheme to serve as a stand-alone solution for providing the precise information of the severity of the DR in an efficient manner.

Modeling global geometric spatial information for rotation invariant classification of satellite images.

The classification of high-resolution satellite images is an open research problem for computer vision research community. In last few decades, the Bag of Visual Word (BoVW) model has been used for the classification of satellite images. In BoVW model, an orderless histogram of visual words without any spatial information is used as image signature. The performance of BoVW model suffers due to this orderless nature and addition of spatial clues are reported beneficial for scene and geographical classification of images. Most of the image representations that can compute image spatial information as are not invariant to rotations. A rotation invariant image representation is considered as one of the main requirement for satellite image classification. This paper presents a novel approach that computes the spatial clues for the histograms of BoVW model that is robust to the image rotations. The spatial clues are calculated by computing the histograms of orthogonal vectors. This is achieved by calculating the magnitude of orthogonal vectors between Pairs of Identical Visual Words (PIVW) relative to the geometric center of an image. The comparative analysis is performed with recently proposed research to obtain the best spatial feature representation for the satellite imagery. We evaluated the proposed research for image classification using three standard image benchmarks of remote sensing. The results and comparisons conducted to evaluate this research show that the proposed approach performs better in terms of classification accuracy for a variety of datasets based on satellite images.

Cross-scale focus measure aggregation in depth recovery of microscopic objects.

In shape from focus (SFF) methods, image focus volume enhancement is an important issue for acquiring accurate depth maps. Mostly, conventional approaches do focus aggregation locally to enhance the focus volume, which may not suppress noisy focus measurements properly and consequently, may provide deteriorated depth maps. Multiresolution fusion-based method is proposed for image focus volume enhancement. First, an initial focus volume is obtained by applying a conventional focus measure. Then, a pyramid of focus volumes is computed using Gaussian filters and subsampling. Focus measures from various focus volumes at different levels are merged into a single resultant focus volume. Finally, the depth map is obtained from the resultant focus volume by maximizing the focus measure in the optical-axis direction. According to the best of my knowledge, the cross-scale aggregation has never been used in enhancing the image focus volume in SFF. The proposed method is evaluated through the experiments using image sequences of real microscopic and simulated objects. Results comparisons based on root mean square error (RMSE) and correlation demonstrate the effectiveness of the proposed method in improving the focus volume and depth map. The proposed fusion method of volumes is a simple but effective. The idea of cross-scale aggregation of focus measures is effective in providing precise focus measures that consequently, provide accurate depth map. In future work, it will further be explored and a more sophisticated and optimization-based fusion algorithm will be applied.

Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering.

OBJECTIVE: Melanoma is a dangerous form of the skin cancer responsible for thousands of deaths every year. Early detection of melanoma is possible through visual inspection of pigmented lesions over the skin, treated with simple excision of the cancerous cells. However, due to the limited availability of dermatologists, the visual inspection alone has the limited and variable accuracy that leads the patient to undergo a series of biopsies and complicates the treatment. In this work, a deep learning method is proposed for automated Melanoma region segmentation using dermoscopic images to overcome the challenges of automated Melanoma region segmentation within dermoscopic images. MATERIALS AND METHODS: A deep region based convolutional neural network (RCNN) precisely detects the multiple affected regions in the form of bounding boxes that simplify localization through Fuzzy C-mean (FCM) clustering. Our method constitutes of three step process: skin refinement, localization of Melanoma region, and finally segmentation of Melanoma. We applied the proposed method on benchmark dataset ISIC-2016 by International Symposium on biomedical images (ISBI) having 900 training and 376 testing Melanoma dermatological images. MAIN FINDINGS: The performance is evaluated for Melanoma segmentation using various quantitative measures. Our method achieved average values of pixel level specificity (SP) as 0.9417, pixel level sensitivity (SE) as 0.9781, F1 _ s core as 0.9589, pixel level accuracy (Ac) as 0.948. In addition, average dice score (Di) of segmentation was recorded as 0.94, which represents good segmentation performance. Moreover, Jaccard coefficient (Jc) averaged value on entire testing images was 0.93. Comparative analysis with the state of art methods and the results have demonstrated the superiority of the proposed method. CONCLUSION: In contrast with state of the art systems, the RCNN is capable to compute deep features with amen representation of Melanoma, and hence improves the segmentation performance. The RCNN can detect features for multiple skin diseases of the same patient as well as various diseases of different patients with efficient training mechanism. Series of experiments towards Melanoma detection and segmentation validates the effectiveness of our method.

An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model.

For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques.

Robust focus measure operator using adaptive log-polar mapping for three-dimensional shape recovery.

Shape from focus (SFF) is a passive optical technique that reconstructs object shape from a sequence of image taken at different focus levels. In SFF techniques, computing focus measurement for each pixel in the image sequence, through a focus measure operator, is the fundamental step. Commonly used focus measure operators compute focus quality in Cartesian space and suffer from erroneous focus quality and lack in robustness. Thus, they provide erroneous depth maps. In this paper, we introduce a new focus measure operator that computes focus quality in log-polar transform (LPT) Properties of LPT, such as biological inspiration, data selection, and edge invariance, enable computation of better focus quality in the presence of noise. Moreover, instead of using a fixed patch of the image, we suggest the use of an adaptive window. The focus quality is assessed by computing variation in LPT. The effectiveness of the proposed technique is evaluated by conducting experiments using image sequences of different simulated and real objects. The comparative analysis shows that the proposed method is robust and effective in the presence of various types of noise.

MRT letter: Guided filtering of image focus volume for 3D shape recovery of microscopic objects.

In this letter, a shape from focus (SFF) method is proposed that utilizes the guided image filtering to enhance the image focus volume efficiently. First, image focus volume is computed using a conventional focus measure. Then each layer of image focus volume is filtered using guided filtering. In this work, the all-in-focus image, which can be obtained from the initial focus volume, is used as guidance image. Finally, improved depth map is obtained from the filtered image focus volume by maximizing the focus measure along the optical axis. The proposed SFF method is efficient and provides better depth maps. The improved performance is highlighted by conducting several experiments using image sequences of simulated and real microscopic objects. The comparative analysis demonstrates the effectiveness of the proposed SFF method.

Phylogenetic position of avian nocturnal and diurnal raptors.

We report three new avian mitochondrial genomes, two from widely separated groups of owls and a falcon relative (the Secretarybird). We then report additional progress in resolving Neoavian relationships in that the two groups of owls do come together (it is not just long-branch attraction), and the Secretarybird is the deepest divergence on the Accipitridae lineage. This is now agreed between mitochondrial and nuclear sequences. There is no evidence for the monophyly of the combined three groups of raptors (owls, eagles, and falcons), and again this is agreed by nuclear and mitochondrial sequences. All three groups (owls, accipitrids [eagles], and falcons) do appear to be members of the "higher land birds," and though there may not yet be full "consilience" between mitochondrial and nuclear sequences for the precise order of divergences of the eagles, falcons, and the owls, there is good progress on their relationships.

Robust depth estimation and image fusion based on optimal area selection.

Mostly, 3D cameras having depth sensing capabilities employ active depth estimation techniques, such as stereo, the triangulation method or time-of-flight. However, these methods are expensive. The cost can be reduced by applying optical passive methods, as they are inexpensive and efficient. In this paper, we suggest the use of one of the passive optical methods named shape from focus (SFF) for 3D cameras. In the proposed scheme, first, an adaptive window is computed through an iterative process using a criterion. Then, the window is divided into four regions. In the next step, the best focused area among the four regions is selected based on variation in the data. The effectiveness of the proposed scheme is validated using image sequences of synthetic and real objects. Comparative analysis based on statistical metrics correlation, mean square error (MSE), universal image quality index (UIQI) and structural similarity (SSIM) shows the effectiveness of the proposed scheme.

MRT letter: a total variation based method for 3D shape recovery of microscopic objects through image focus.

Generally, shape from focus methods use a single focus measure to compute focus quality and to obtain an initial depth map of an object. However, different focus measures perform differently in diverse conditions. Therefore, it is hard to get accurate 3D shape based on a single focus measure. In this article, we propose a total variation based method for recovering 3D shape of an object by combining multiple depth hypothesis obtained through different focus measures. Improved performance of the proposed method is evaluated by conducting several experiments using images of synthetic and real microscopic objects. Comparative analysis demonstrates the effectiveness of the proposed approach.

Searching surface orientation of microscopic objects for accurate 3D shape recovery.

In this article, we propose a new shape from focus (SFF) method to estimate 3D shape of microscopic objects using surface orientation cue of each object patch. Most of the SFF algorithms compute the focus value of a pixel from the information of neighboring pixels lying on the same image frame based on an assumption that the small object patch corresponding to the small neighborhood of a pixel is a plane parallel to the focal plane. However, this assumption fails in the optics with limited depth of field where the neighboring pixels of an image have different degree of focus. To overcome this problem, we try to search the surface orientation of the small object patch corresponding to each pixel in the image sequence. Searching of the surface orientation is done indirectly by principal component analysis. Then, the focus value of each pixel is computed from the neighboring pixels lying on the surface perpendicular to the corresponding surface orientation. Experimental results on synthetic and real microscopic objects show that the proposed method produces more accurate 3D shape in comparison to the existing techniques.

Nonlinear approach for enhancement of image focus volume in shape from focus.

Mostly, shape-from-focus algorithms use local averaging using a fixed rectangle window to enhance the initial focus volume. In this linear filtering, the window size affects the accuracy of the depth map. A small window is unable to suppress the noise properly, whereas a large window oversmoothes the object shape. Moreover, the use of any window size smoothes focus values uniformly. Consequently, an erroneous depth map is obtained. In this paper, we suggest the use of iterative 3-D anisotropic nonlinear diffusion filtering (ANDF) to enhance the image focus volume. In contrast to linear filtering, ANDF utilizes the local structure of the focus values to suppress the noise while preserving edges. The proposed scheme is tested using image sequences of synthetic and real objects, and results have demonstrated its effectiveness.