•ZnO nanoparticles were prepared by hydrothermal method for various solution pH.•The synthesized ZnO nanoparticles exhibit hexagonal wurtzite structure.•Morphology of ZnO nanoparticle was effectively ...influenced by solution pH.•94% of RhB dye degradation was observed for ZnO nanostructure prepared at pH 9.
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ZnO nanoparticles were prepared by hydrothermal method from the source materials of zinc chloride and ammonium hydroxide. Precursor solution pH was varied to 7, 9, 11 and 13 by the addition of ammonium solution and the solution was hydrothermally treated at 150°C for 3h. Further prepared samples were annealed at 400°C for 3h. X-ray diffraction technique was employed to study the structure and crystalline nature of synthesized nanoparticles. Diffuse Reflectance Spectroscopy studies revealed that optical band gap of ZnO is slightly varied due to the effect of size of the particle. Field emission scanning electron microscope images showed that the prepared ZnO nanoparticles acquired spindle like nanorods, hexagonal disk, porous nanorods and nanoflower structures due to the effect of pH of the precursor solution. Photocatlytic activity of the prepared ZnO nanoparticles was evaluated for Rhodamine B (RhB) dye which showed 94% of degradation and good stability for five cycles.
We introduce a detection framework for dense crowd counting and eliminate the need for the prevalent density regression paradigm. Typical counting models predict crowd density for an image as opposed ...to detecting every person. These regression methods, in general, fail to localize persons accurate enough for most applications other than counting. Hence, we adopt an architecture that locate s every person in the crowd, size s the spotted heads with bounding box and then count s them. Compared to normal object or face detectors, there exist certain unique challenges in designing such a detection system. Some of them are direct consequences of the huge diversity in dense crowds along with the need to predict boxes contiguously. We solve these issues and develop our LSC-CNN model, which can reliably detect heads of people across sparse to dense crowds. LSC-CNN employs a multi-column architecture with top-down feature modulation to better resolve persons and produce refined predictions at multiple resolutions. Interestingly, the proposed training regime requires only point head annotation, but can estimate approximate size information of heads. We show that LSC-CNN not only has superior localization than existing density regressors, but outperforms in counting as well. The code for our approach is available at https://github.com/val-iisc/lsc-cnn .
Reactive oxygen species (ROS) are highly reactive oxygen-containing chemical species formed as a by-product of normal aerobic respiration and also from a number of other cellular enzymatic reactions. ...ROS function as key mediators of cellular signaling pathways involved in proliferation, survival, apoptosis, and immune response. However, elevated and sustained ROS production promotes tumor initiation by inducing DNA damage or mutation and activates oncogenic signaling pathways to promote cancer progression. Recent studies have shown that ROS can facilitate carcinogenesis by controlling microRNA (miRNA) expression through regulating miRNA biogenesis, transcription, and epigenetic modifications. Likewise, miRNAs have been shown to control cellular ROS homeostasis by regulating the expression of proteins involved in ROS production and elimination. In this review, we summarized the significance of ROS in cancer initiation, progression, and the regulatory crosstalk between ROS and miRNAs in cancer.
Deep convolutional neural networks (CNNs) have revolutionized the computer vision research and have seen unprecedented adoption for multiple tasks, such as classification, detection, and caption ...generation. However, they offer little transparency into their inner workings and are often treated as black boxes that deliver excellent performance. In this paper, we aim at alleviating this opaqueness of CNNs by providing visual explanations for the network's predictions. Our approach can analyze a variety of CNN-based models trained for computer vision applications, such as object recognition and caption generation. Unlike the existing methods, we achieve this via unraveling the forward pass operation. The proposed method exploits feature dependencies across the layer hierarchy and uncovers the discriminative image locations that guide the network's predictions. We name these locations CNN fixations, loosely analogous to human eye fixations. Our approach is a generic method that requires no architectural changes, additional training, or gradient computation, and computes the important image locations (CNN fixations). We demonstrate through a variety of applications that our approach is able to localize the discriminative image locations across different network architectures, diverse vision tasks, and data modalities.
Understanding and predicting the human visual attention mechanism is an active area of research in the fields of neuroscience and computer vision. In this paper, we propose DeepFix, a fully ...convolutional neural network, which models the bottom-up mechanism of visual attention via saliency prediction. Unlike classical works, which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts the saliency map in an end-to-end manner. DeepFix is designed to capture semantics at multiple scales while taking global context into account, by using network layers with very large receptive fields. Generally, fully convolutional nets are spatially invariant-this prevents them from modeling location-dependent patterns (e.g., centre-bias). Our network handles this by incorporating a novel location-biased convolutional layer. We evaluate our model on multiple challenging saliency data sets and show that it achieves the state-of-the-art results.
Machine learning models are susceptible to adversarial perturbations: small changes to input that can cause large changes in output. It is also demonstrated that there exist input-agnostic ...perturbations, called universal adversarial perturbations, which can change the inference of target model on most of the data samples. However, existing methods to craft universal perturbations are (i) task specific, (ii) require samples from the training data distribution, and (iii) perform complex optimizations. Additionally, because of the data dependence, fooling ability of the crafted perturbations is proportional to the available training data. In this paper, we present a novel, generalizable and data-free approach for crafting universal adversarial perturbations. Independent of the underlying task, our objective achieves fooling via corrupting the extracted features at multiple layers. Therefore, the proposed objective is generalizable to craft image-agnostic perturbations across multiple vision tasks such as object recognition, semantic segmentation, and depth estimation. In the practical setting of black-box attack scenario (when the attacker does not have access to the target model and it's training data), we show that our objective outperforms the data dependent objectives to fool the learned models. Further, via exploiting simple priors related to the data distribution, our objective remarkably boosts the fooling ability of the crafted perturbations. Significant fooling rates achieved by our objective emphasize that the current deep learning models are now at an increased risk, since our objective generalizes across multiple tasks without the requirement of training data for crafting the perturbations. To encourage reproducible research, we have released the codes for our proposed algorithm.1
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Highly conductive Copper/carbon nanotubes (Cu/CNT) composites are being explored in structural, electrical and thermal applications due to their requirement for prolonged usage in ...industrial applications. The conventional materials used to transport the heat and heat transfer fluid are required to be strengthened to increase their structural integrity and longevity in addition to increase their performances. Thus, the objective of present work is to study the influence of different diameters of CNT in improving the mechanical, electrical and thermal properties of Copper based composites by varying the sintering duration and CNT concentration in order to explore their usage in different potential applications. In this study, Cu/CNT composite powder was synthesized using molecular level mixing technique and consolidated by uniaxial compaction at 800 MPa followed by microwave sintering at 60, 75 and 90 min. Three diameters of CNT having the range of 10–20 nm, 20–40 nm and 40–60 nm were used with 0.25, 0.5, 0.75 and 1 wt.% concentrations to prepare the composites. The superior characteristics obtained from the present study are as follows: Hardness- 91.7 ± 1.59 VHN, Relative density– 90.9%, Electrical conductivity– 49.3 ± 0.1 MS/m, Thermal conductivity– 364.5 W/mK. It is concluded that any desired electrical and thermal characteristics of the composites can be obtained using the proposed methodology and the results obtained from the present study are at par with other high end technology.
Bahrain's population consists mainly of Arabs, Baharna and Persians leading Bahrain to become ethnically diverse. The exploration of the ethnic origin and genetic structure within the Bahraini ...population is fundamental mainly in the field of population genetics and forensic science. The purpose of the study was to investigate and conduct genetic studies in the population of Bahrain to assist in the interpretation of DNA-based forensic evidence and in the construction of appropriate databases. 24 short-tandem repeats in the GlobalFiler PCR Amplification kit including 21 autosomal STR loci and three gender determination loci were amplified to characterize different genetic and forensic population parameters in a cohort of 543 Bahraini unrelated healthy men. Samples were collected during the year 2017. The genotyping of the 21 autosomal STRs showed all of the loci were in Hardy-Weinberg Equilibrium (HWE) after applying Bonferroni's correction. We also found out no significant deviations from LD between pairwise STR loci in Bahraini population except when plotting for D3S1358-CSF1PO, CSF1PO-SE33, D19S433-D12S391, FGA-D2S1338, FGA-SE33, FGA-D7S820 and D7S820-SE33. The SE33 locus was the most polymorphic for the studied population and THO1 locus was the less polymorphic. The Allele 8 in TPOX scored the highest allele frequency of 0.496. The SE33 locus showed the highest power of discrimination (PD) in Bahraini population, whereas TPOX showed the lowest PD value. The 21 autosomal STRs showed a value of combined match probability (CMP) equal to 4.5633E-27, and a combined power of discrimination (CPD) of 99.99999999%. Off-ladders and tri-allelic variants were observed in various samples at D12S391, SE33 and D22S1045 loci. Additionally, pairwise genetic distances based on FST were calculated between Bahraini population and other populations extracted from the literature. Genetic distances were represented in a non-metric MDS plot and clustering of populations according to their geographic locations was detected. Phylogenetic tree was constructed to investigate the genetic relatedness between Bahraini population and the neighboring populations. Our study indicated that the twenty-one autosomal STRs are highly polymorphic in the Bahraini population and can be used as a powerful tool in forensics and population genetic analyses including paternity testing and familial DNA searching.
To increase agriculture production, accurate and fast detection of plant disease is required. Expert advice is needed to detect disease in plants, nutrition deficiencies or any other abnormalities ...caused by extreme weather conditions. But this process is very tedious, costly, and takes more time. In this paper, hyperspectral imaging and machine learning were used to detect different stages (early, middle, and critical stage) of the powderly mildew disease (PMD) in squash plants. An unmanned aerial vehicle (UAV) was used to collect the data from the field and Locality Preserving Discriminative Broad Learning (LPDBL) was used to distinguish the diseased and healthy plants. In addition, the ability to detect the diseased plant by the proposed method was evaluated using 10 different spectral vegetation indices (VIs). The results show the proposed method detected the disease accurately in the early, middle, and critical stages of the squash plant. The proposed method’s performance is compared with six different PMDs under indoor laboratory test and UAV-based field test conditions. The comparison’s results show that the LPDBL provides better accuracy in detecting disease in the squash plant.
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