Development of a Demand Driven Hydro-climatic Model for Drought Planning

In recent years, droughts with increasing severity and frequency have been experienced around the world due to climate change effects. Water planning and management during droughts needs to deal with water demand variability, uncertainties in streamflow prediction, conflicts over water resources allocation, and the absence of necessary emergency schemes in drought situations. Reservoirs could play an important role in drought mitigation; therefore, development of an algorithm for operation of reservoirs in drought periods could help to mitigate the drought impacts by reducing the expected water shortages. For this purpose, the probable drought’s characteristics and their variations in response to factors such as climate change should be incorporated. This study aims at developing a contingency planning scheme for operation of reservoirs in drought periods using hedging rules with the objective of decreasing the maximum water deficit. The case study for evaluation of the performance of the proposed algorithm is the Sattarkhan reservoir in the Aharchay watershed, located in the northwestern part of Iran. The trend evaluations of the hydro-climatic variables show that the climate change has already affected streamflow in the region and has increased water scarcity and drought severity. To incorporate the climate change study in reservoir planning; streamflow should be simulated under climate change impacts. For this purpose, the climatic variables including temperature and precipitation in the future under climate change impacts are simulated using downscaled GCM (General Circulation Model) outputs to derive scenarios for possible future drought events. Then a hydrological model is developed to simulate the river streamflow, based on the downscaled data. The results show that the proposed methodology leads to less water deficit and decreases the drought damages in the study area.     

pH Responsive Abelmoschus esculentus Mucilage and Administration of Methotrexate: In-Vitro Antitumor and In-Vivo Toxicity Evaluation

The rapid progression in biomaterial nanotechnology apprehends the potential of non-toxic and potent polysaccharide delivery modules to overcome oral chemotherapeutic challenges. The present study is aimed to design, fabricate and characterize polysaccharide nanoparticles for methotrexate (MTX) delivery. The nanoparticles (NPs) were prepared by Abelmoschus esculentus mucilage (AEM) and chitosan (CS) by the modified coacervation method, followed by ultra-sonification. The NPs showed much better pharmaceutical properties with a spherical shape and smooth surface of 213.4–254.2 nm with PDI ranging between 0.279–0.485 size with entrapment efficiency varying from 42.08 ± 1.2 to 72.23 ± 2.0. The results revealed NPs to possess positive zeta potential and a low polydispersity index (PDI). The in-vitro drug release showed a sustained release of the drug up to 32 h with pH-dependence. Blank AEM -CS NPs showed no in-vivo toxicity for a time duration of 14 days, accompanied by high cytotoxic effects of optimized MTX loaded NPs against MCF-7 and MD-MBA231 cells by MTT assay. In conclusion, the findings advocated the therapeutic potential of AEM/CS NPs as an efficacious tool, offering a new perspective for pH-responsive routing of anticancer drugs with tumor cells as a target.     

Underlay Device to Device Communication with Imperfect Interference Channel Knowledge

The objective of any night vision system is to enable a person to see in the dark. A low-contrast image puts a contrast constraint on the human observer visibility at night. This is the basic reason for the large number of accidents at night. This research presents two proposed approaches to enhance the visibility of the infrared (IR) night vision images through an efficient histogram processing. The first approach is based on contrast limited adaptive histogram equalization. The second proposed approach depends on histogram matching. The histogram matching uses a reference visual image for converting night vision images into good quality images. The obtained results are evaluated with quality metrics such as entropy, average gradient, contrast improvement factor and sobel edge magnitude.     

New exact solutions of the Tzitzéica-type equations in non-linear optics using the expa function method

One specific class of non-linear evolution equations, known as the Tzitzéica-type equations, has received great attention from a group of researchers involved in non-linear science. In this article, new exact solutions of the Tzitzéica-type equations arising in non-linear optics, including the Tzitzéica, Dodd–Bullough–Mikhailov and Tzitzéica–Dodd–Bullough equations, are obtained using the exp_a function method. The integration technique actually suggests a useful and reliable method to extract new exact solutions of a wide range of non-linear evolution equations.     

BSig: evaluating the statistical significance of biclustering solutions

Statistical evaluation of biclustering solutions is essential to guarantee the absence of spurious relations and to validate the high number of scientific statements inferred from unsupervised data analysis without a proper statistical ground. Most biclustering methods rely on merit functions to discover biclusters with specific homogeneity criteria. However, strong homogeneity does not guarantee the statistical significance of biclustering solutions. Furthermore, although some biclustering methods test the statistical significance of specific types of biclusters, there are no methods to assess the significance of flexible biclustering models. This work proposes a method to evaluate the statistical significance of biclustering solutions. It integrates state-of-the-art statistical views on the significance of local patterns and extends them with new principles to assess the significance of biclusters with additive, multiplicative, symmetric, order-preserving and plaid coherencies. The proposed statistical tests provide the unprecedented possibility to minimize the number of false positive biclusters without incurring on false negatives, and to compare state-of-the-art biclustering algorithms according to the statistical significance of their outputs. Results on synthetic and real data support the soundness and relevance of the proposed contributions, and stress the need to combine significance and homogeneity criteria to guide the search for biclusters.     

Exercise classification and event segmentation in Hammersmith Infant Neurological Examination videos

Image and video processing techniques are being frequently used in medical science applications. Computer vision-based systems have successfully replaced various manual medical processes such as analyzing physical and biomechanical parameters, physical examination of patients. These systems are gaining popularity because of their robustness and the objectivity they bring to various medical procedures. Hammersmith Infant Neurological Examinations (HINE) is a set of physical tests that are carried out on infants in the age group of 3–24 months with neurological disorders. However, these tests are graded through visual observations, which can be highly subjective. Therefore, computer vision-aided approach can be used to assist the experts in the grading process. In this paper, we present a method of automatic exercise classification through visual analysis of the HINE videos recorded at hospitals. We have used scale-invariant-feature-transform features to generate a bag-of-words from the image frames of the video sequences. Frequency of these visual words is then used to classify the video sequences using HMM. We also present a method of event segmentation in long videos containing more than two exercises. Event segmentation coupled with a classifier can help in automatic indexing of long and continuous video sequences of the HINE set. Our proposed framework is a step forward in the process of automation of HINE tests through computer vision-based methods. We conducted tests on a dataset comprising of 70 HINE video sequences. It has been found that the proposed method can successfully classify exercises with accuracy as high as 84%. The proposed work has direct applications in automatic or semiautomatic analysis of “vertical suspension” and “ventral suspension” tests of HINE. Though some of the critical tests such as “pulled-to-sit,” “lateral tilting,” or “adductor’s angle measurement” have already been addressed using image- and video-guided techniques, scopes are there for further improvement.     

A Review of Pathways for Building Fire Spread in the Wildland Urban Interface Part II: Response of Components and Systems and Mitigation Strategies in the United States

Structure loss in wildland fires has significantly increased over the past few decades, affected by increased development in rural areas, changing fuel management policies, and climate change, all of which are projected to increase in the future. This paper is Part II of a two-part review, which presents a summary of fundamental and applied research on pathways to fire spread in the wildland urban interface. Part I discussed the fundamentals of wildland fire spread via radiative heat transfer, direct flame contact, and firebrand exposure. Here in Part II, we cover the response of building components and systems, as well as mitigation strategies used to prevent fire spread into and within communities in the United States. Post-fire investigations, full-scale structural testing, individual component testing, and combined systems or assembly testing have been used to identify building component and system vulnerabilities such as roofs, vents, siding, decks, fences, and mulch. Using results from these tests and investigations at different scales, some knowledge has been gained on specific vulnerabilities and the effectiveness of mitigation strategies, but a quantitative framework has not yet been established. On a community level, the layout of structures and the space between them has been shown to be incredibly important in mitigating wildfire risk. More locally, defensible space around homes has been effective in mitigating exposure from both radiation and direct flame contact. Firebrands still remain a challenge; however, many design recommendations have been proposed to harden structures against firebrand exposures. Recommendations for future research and development are also presented.     

Quasi-static compressive behaviour of aluminium cenosphere syntactic foams

In this paper, cenosphere particles embedded in AA2014 aluminium matrix are used to fabricate syntactic foam by stir casting method. The particle size is about 100?µm and foam density is about 1990?kg?m^?3. Compression tests at strain rate 0.001/s are performed on foam samples to characterise their mechanical properties which are then used in numerical analysis on commercial finite element analysis software ABAQUS/CAE with isotropic elastic-plastic material model. Experimental and numerical results show good conformity in deformation behaviour with elastic and plateau zones showing average deviations less than 5% and 20%, respectively. Foams showed high yield stress and energy absorption capabilities that can be useful in making blast and impact resistant structures.     

On the buckling properties of concentric carbon/boron-nitride multi-walled nanotubes: A finite element investigation

Finite element modeling approach is used here to investigate the behavior of concentric multi-walled boron-nitride and carbon nanotubes under the compressive loadings. Double-walled and triple-walled concentric boron-nitride and carbon nanotubes with different arrangements and geometries are considered. It is shown that multi-walled boron-nitride nanotubes lose their stabilities at larger compressive forces than other arrangements in which at least one carbon wall exists. Comparing the armchair and zig-zag multi-walled nanotubes, the latter one has larger buckling force than the former one. It is also shown that the nanotubes with smaller radii have larger critical compressive forces than those with larger radii.