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This paper is devoted to studying the following nonlinear biharmonic Schrödinger equation with combined power-type nonlinearities Δ 2 u − λ u = μ | u | q − 2 u + | u | 4 ∗ − 2 u in R N , where N ≥ 5 , μ > 0 , 2 < q < 2 + 8 N , 4 ∗ = 2 N N − 4 is the H 2 -critical Sobolev exponent, and λ appears as a Lagrange multiplier. By analyzing the behavior of the ground state energy with respect to the prescribed mass, we establish the existence of normalized ground state solutions. Furthermore, all ground states are proved to be local minima of the associated energy functional. [ABSTRACT FROM AUTHOR]

Abstract: C60 forms colloidally stable nanoparticles (nC60) via extended mixing or solvent exchange. Many studies on the environmental impacts of nC60 use aliquots from a large batch of nC60 suspension and either dilute them or subject them to other modifications under the assumption that the properties of the parent suspension remain stable over time and are unaltered by these manipulations. In the present study, nC60 produced via stirring in the presence of sodium citrate (cit/nC60) was characterized with respect to particle size, surface charge, and morphology following dilution. Counter-intuitively, the results show that the colloidal properties of diluted cit/nC60 are not fixed despite constant solution chemistry and are dependent upon the ratios of [C60] to [Na+] and [citrate]. In some cases, diluted nC60 had significantly different morphology. This study suggests that any experiment involving modifications of stock nC60 suspensions must take the altered colloidal properties of the diluted nC60 into consideration. [Copyright &y& Elsevier]

The discovery that negatively charged aggregates of C60 fullerene (nC60) are stable in water has raised concerns regarding the potential environmental and health effects of these aggregates. In this work, we show that nC60 aggregates produced by extended mixing in the presence of environmentally relevant carboxylic acids (acetic acid, tartaric acid, citric acid) have surface charge and morphologic properties that differ from those produced by extended mixing in water alone. In general, aggregates formed in the presence of these acids have a more negative surface charge and are more homogeneous than those produced in water alone. Carboxylic acid identity, solution pH, and sodium ion concentration, which are all intricately coupled, play an important role in setting the measured surface charge. Comparisons between particle sizes determined by analysis of TEM images and those obtained by dynamic light scattering (DLS) indicate that DLS results require careful evaluation when used to describe nC60 aggregates. [Copyright &y& Elsevier]

We consider the following gradient system of Choquard type − Δ u 1 + u 1 = (I μ ∗ F (u 2)) f (u 1) in R N , − Δ u 2 + u 2 = (I μ ∗ F (u 1)) f (u 2) in R N , where N ≥ 3 and I μ (0 < μ < N) is a Riesz potential. By the variational methods, we prove the existence of ground state solutions under the Berestycki–Lions type conditions. [ABSTRACT FROM AUTHOR]

• SOC of semiarid and arid regions are more sensitive to changes in the climatic variable. • Only five agro-climatic zones (out of 14) were resilient to disturbance. • Soils from semiarid and arid zones were found associated with lower SOC content. Soils are a binding site for carbon storage. Climatic variables, namely precipitation, and temperature are regarded as the primary factors controlling soil organic carbon (SOC) storage; however, no consensus has been made about the magnitude and direction that changes in climatic variables may have on SOC. Based on copula theory, the present study investigates the soil carbon dynamics and the likelihood of SOC occurrence under varying climatic conditions across India's 14 agro-climatic zones. Results demonstrate the possibility of occurrence of SOC under both low and high temperature/precipitation conditions. It was found that the SOC of agro-climatic zones situated in semi-arid and arid regions are more sensitive to changes in climatic variables compared to that of the others. We then quantify the soil resilience of the agro-climatic zones based on the amount of SOC content. Results showed that only 1/3 of India's agro-climatic zones were resilient during the study period (1985–2005). Thus, the study's findings facilitate the identification of India's most sensitive agro-climatic zone for soil carbon management and climate-related policy. It stresses the need for big data assimilation to identify site-specific management practices that can facilitate soil health and improve the country's soil resilient capacity for food security and environmental management. [ABSTRACT FROM AUTHOR]

• IoT is a revolutionary technology with innumerable applications in industrial sectors. • Potential benefits of big data to small and medium size enterprises are discussed. • Effects of integrating IoT and Big data on the SMEs are presented. • An approach to detect XSS attack for protecting user's privacy is proposed. • Implementation details and outcomes that prove the efficiency of proposed approach are provided. Advancement in embedded Nano-technologies empowers IoT technology which serves as the backbone technology for many small and medium enterprises. Evolution of smart devices proved as a supreme source of data, denoted as Big data, that is analyzed to instantly extricate constructive and valuable information that helps the organization in many ways. As it may embrace sensitive information, hence, it turned out to be the fascinating target of multitude of attacks that aims at stealing the information, resulting into privacy breaches. XSS attack is one such security attack that supports the attacker to intrude into user's personal space. Therefore, this paper is focused on designing an approach that detects XSS attack in IoT network to protect the data privacy. It employs Convolution Neural Network (CNN) to detect the XSS attack payload after applying certain data preparation methods. This approach helps in preventing privacy breaches which eventually helps the enterprises in strengthening their bond with their users. The experimental results unveiled that the approach achieves a detection accuracy of 99% after the successful execution of data preparation methods. [ABSTRACT FROM AUTHOR]

• We propose an end-to-end framework for few-shot activity recognition, which consists deep embedding module, cross-modal memory module, and the few-shot activity recognition module. • We design an innovative cross-modal memory structure, where each memory slot is an visual-textual embedding pair that stores the multi-modal semantic information for one activity attribute. • We conduct extensive experiments on datasets HMDB51 and UCF101 to illustrate the effectiveness and superiority of the cross-modal memory network. Deep learning based action recognition methods require large amount of labelled training data. However, labelling large-scale video data is time consuming and tedious. In this paper, we consider a more challenging few-shot action recognition problem where the training samples are few and rare. To solve this problem, memory network has been designed to use an external memory to remember the experience learned in training and then apply it to few-shot prediction during testing. However, existing memory-based methods just update the visual information with fixed label embeddings in the memory, which cannot adapt well to novel activities during testing. To alleviate the issue, we propose a novel end-to-end cross-modal memory network for few-shot activity recognition. Specifically, the proposed memory architecture stores the dynamic visual and textual semantics for some high-level attributes related to human activities. And the learned memory can provide effective multi-modal information for new activity recognition in the testing stage. Extensive experimental results on two video datasets, including HMDB51 and UCF101, indicate that our method could achieve significant improvements over other previous methods. [ABSTRACT FROM AUTHOR]

Abstract Zero-shot complex event detection has been an emerging task in coping with the scarcity of labeled training videos in practice. Aiming to progress beyond the state-of-the-art zero-shot event detection, we propose a new zero-shot event detection approach, which exploits the semantic correlation between an event and concepts. Based on the concept detectors pre-trained from external sources, our method learns the semantic correlation from the concept vocabulary and emphasizes on the most related concepts for the zero-shot event detection. Particularly, a novel Event-Adaptive Concept Integration algorithm is introduced to estimate the effectiveness of semantically related concepts by assigning different weights to them. As opposed to assigning weights by an invariable strategy, we compute the weights of concepts using the area under score curve. The assigned weights are incorporated into the confidence score vector statistically to better characterize the event-concept correlation. Our algorithm is proved to be able to harness the related concepts discriminatively tailored for a target event. Extensive experiments are conducted on the challenging TRECVID event video datasets, which demonstrate the advantage of our approach over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Learning the distance metric between pairs of examples is of great importance for visual recognition, especially for person re-identification (Re-Id). Recently, the contrastive and triplet loss are proposed to enhance the discriminative power of the deeply learned features, and have achieved remarkable success. As can be seen, either the contrastive or triplet loss is just one special case of the Euclidean distance relationships among these training samples. Therefore, we propose a structured graph Laplacian embedding algorithm, which can formulate all these structured distance relationships into the graph Laplacian form. The proposed method can take full advantages of the structured distance relationships among these training samples, with the constructed complete graph. Besides, this formulation makes our method easy-to-implement and super-effective. When embedding the proposed algorithm with the softmax loss for the CNN training, our method can obtain much more robust and discriminative deep features with inter-personal dispersion and intra-personal compactness, which is essential to person Re-Id. We did experiments on top of three popular networks, namely AlexNet [1], DGDNet [2] and ResNet50 [3], on recent four widely used Re-Id benchmark datasets, and it shows that the proposed structure graph Laplacian embedding is very effective. [ABSTRACT FROM AUTHOR]

Carbon nanotubes (CNTs) have great potential in industrial, consumer, and mechanical applications, based partly on their unique structural, optical and electronic properties. CNTs are commonly oxidized or treated with surfactants to facilitate aqueous solution processing, and these CNT surface modifications also increase possible human and ecological exposures to nanoparticle-contaminated waters. To determine the exposure outcomes of oxidized and surfactant-wrapped multiwalled carbon nanotubes (MWCNTs) on biochemical processes, metabolomics-based profiling of human liver cells (C3A) was utilized. Cells were exposed to 0, 10, or 100 ng/mL of MWCNTs for 24 and 48 h; MWCNT particle size distribution, charge, and aggregation were monitored concurrently during exposures. Following MWCNT exposure, cellular metabolites were extracted, lyophilized, and buffered for 1 H NMR analysis. Acquired spectra were subjected to both multivariate and univariate analysis to determine the consequences of nanotube exposure on the metabolite profile of C3A cells. Resulting scores plots illustrated temporal and dose-dependent metabolite responses to all MWCNTs tested. Loadings plots coupled with t -test filtered spectra identified metabolites of interest. XPS analysis revealed the presence of hydroxyl and carboxyl functionalities on both MWCNTs surfaces. Metal content analysis by ICP-AES indicated that the total mass concentration of the potentially toxic impurities in the exposure experiments were extremely low ( i.e. [Ni] ≤ 2 × 10 − 10 g/mL). Preliminary data suggested that MWCNT exposure causes perturbations in biochemical processes involved in cellular oxidation as well as fluxes in amino acid metabolism and fatty acid synthesis. Dose-response trajectories were apparent and spectral peaks related to both dose and MWCNT dispersion methodologies were determined. Correlations of the significant changes in metabolites will help to identify potential biomarkers associated with carbonaceous nanoparticle exposure. [ABSTRACT FROM AUTHOR]

• Design an approach to detect DDoS attack in consumer IoT networks. • Prepare a dataset containing relevant IoT network features for enhanced attack detection. • Attack is detected using prominent Machine Learning classifiers. • Performance assessment is completed on proposed dataset to illuminate its detection efficacy. From smart home to industrial automation to smart power grid, IoT- based solutions penetrate into every working field. These devices expand the attack surface and turned out to be an easy target for the attacker as resource constraint nature hinders the integration of heavy security solutions. Because IoT devices are less secured and operate mostly in unattended scenario, they perfectly justify the requirements of attacker to form botnet army to trigger Denial of Service attack on massive scale. Therefore, this paper presents a Machine Learning-based attack detection approach to identify the attack traffic in Consumer IoT (CIoT). This approach operates on local IoT network-specific attributes to empower low-cost machine learning classifiers to detect attack, at the local router. The experimental outcomes unveiled that the proposed approach achieved the highest accuracy of 0.99 which confirms that it is robust and reliable in IoT networks. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: Quantifying the mobility of engineered nanoparticles in hydrologic pathways from point of release to human or ecological receptors is essential for assessing environmental exposures. Column transport experiments are a widely used technique to estimate the transport parameters of engineered nanoparticles in the subsurface environment, but this technique is often time-consuming, labor-intensive, and of low sample throughput. Thus, the traditional column experiment is unlikely to be a viable tool for processing the large numbers of engineered nanomaterials in various types of porous media that will be needed for environmental impact assessment and regulatory activities. Here we present a high throughput screening technique for nanoparticle transport using 96 deep well plate columns packed with porous media. The technique was tested for the transport of 60-nm polystyrene microspheres, fullerene C60 nanoparticles (aq/nC60), and surfactant-wrapped single-walled carbon nanotubes (SWNTs) in 0.001–0.1% sodium dodecyl sulfate (SDS) through Iota quartz sand and Calls Creek sediment. Our results showed that this screening technique produced highly reproducible column hydrodynamic properties as revealed by conservative tracer tests and precise measurements of nanoparticle transport parameters. Additionally, all nanoparticles exhibited greater retention in the sediment than in Iota quartz, and the retention of SDS-SWNTs decreased with increasing SDS concentrations, which is consistent with the existing literature. We conclude that this technique is well suited for rapidly screening the mobility of engineered nanomaterials in porous media. [Copyright &y& Elsevier]

Recently, the emerging graph convolutional network (GCN) has been applied into fault diagnosis with the aim of providing additional fault features through topological information. However, there are some limitations with these methods. First, the interactions between multi-frequency scales are ignored in existing studies, while they mainly focus on constructing graphs through the relationship between channels/instances. Second, the constructed graph cannot well reflect the topology of noisy samples and lacks robust hierarchical representation learning capability, and the learned graphs have limited interpretability. Hence, a Multi-Scale GCN with Contrastive-learning enhanced Self-attention Pooling (MSGCN-CSP) method is proposed for intelligent fault diagnosis of gearbox. Time–frequency distributions are converted into multi-scale graphs to extract fault features through topological relationships between multi-frequencies. Contrastive-learning is used to implement graph pooling, which enables hierarchical representation learning. Experimental results on two gearbox datasets illustrate that the proposed method offers competitive diagnostic performance and provides good interpretability in establishing GCN. [Display omitted] • A graph modeling strategy is proposed for perceiving multi-scale frequency information. • The contrastive-learning enhanced pooling is proposed for robust hierarchical representation learning. • Physical mechanism of the method is analyzed to demonstrate its interpretability. • Comparative studies show the superiority of the method in terms of accuracy. [ABSTRACT FROM AUTHOR]

As an important branch of multimedia content analysis, Surveillance Event Detection (SED) is still a quite challenging task due to high abstraction and complexity such as occlusions, cluttered backgrounds and viewpoint changes etc. To address the problem, we propose a unified SED detection framework which divides events into two categories, i.e., short-term events and long-duration events. The former can be represented as a kind of snapshots of static key-poses and embodies an inner-dependencies, while the latter contains complex interactions between pedestrians, and shows obvious inter-dependencies and temporal context. For short-term event, a novel cascade Convolutional Neural Network (CNN)–HsNet is first constructed to detect the pedestrian, and then the corresponding events are classified. For long-duration event, Dense Trajectory (DT) and Improved Dense Trajectory (IDT) are first applied to explore the temporal features of the events respectively, and subsequently, Fisher Vector (FV) coding is adopted to encode raw features and linear SVM classifiers are learned to predict. Finally, a heuristic fusion scheme is used to obtain the results. In addition, a new large-scale pedestrian dataset, named SED-PD, is built for evaluation. Comprehensive experiments on TRECVID SEDtest datasets demonstrate the effectiveness of proposed framework. [ABSTRACT FROM AUTHOR]

• An adaptive local variances-based coefficient is proposed to distinguish noise points and the edge of an object, which improves the segmentation accuracy of medical images with intensity inhomogeneity and noises. • The edge detection function of the LRFLSM model is reverted to a simple detection function so that the edge detection function does not need to update with iteration. • The two-layer local variances-based level set framework for segmenting left ventricles and left epicardium simultaneously. • Experimental results for medical images and synthetic images show the desirable performance of the ALVLS model in accuracy, efficiency, and robustness to noise. Medical image segmentation is a very challenging task, not only because the intensity of the medical image itself is not uniform, but also it may be accompanied by the impact of noise. Although mathematics, computer science, medicine, and other interdisciplinary fields have begun to study the problem of medical image segmentation, and have put forward a variety of segmentation algorithms, there is still much room for further improvement and enhancement. In the process of medical image collection and reconstruction, it is easy to produce intensity inhomogeneity and noises, as well as interference from other tissues, resulting in the difficulty of accurate segmentation. In this paper, we propose the adaptive local variances-based level set (ALVLS) model to segment medical images with intensity inhomogeneity and noises, including cardiac MR images, brain MR images, and breast ultrasound images. According to the variance difference information, the ALVLS model can adjust the effect of the area term adaptively. The local intensity variances are designed to optimize the ability to resist noise, which improves the segmentation accuracy of medical images. We also propose the two-layer level set model for segmenting left ventricles and left epicardium simultaneously. Experimental results for medical images and synthetic images show the desirable performance of the ALVLS model in accuracy, efficiency, and robustness to noise. In medical image competition, the Dice coefficient is used to calculate the similarity between the segmentation result and the ground truth. Thus we do comparisons with other methods and show that the Dice coefficient of the proposed method is higher than other testing methods. [ABSTRACT FROM AUTHOR]

This paper probes the role of hydroxyl radicals (·OH) generated by H 2 O 2 photolysis on graphene oxide (GO) phototransformation, under simulated sunlight. It focuses on comparing the photoreaction of GO with (indirect) and without (direct) added H 2 O 2 under simulated sunlight conditions. The biomarker responses of fish epithelial cells in in vitro assays of parent GO and GO photoreacted with H 2 O 2 and their interaction with model biomembranes are also compared. GO was found to be far more extensively photodecomposed in the presence of H 2 O 2, with ∼85% of the initial carbon content converted to CO 2 during 48 h of irradiation. Direct and indirect photoreactions occurred concurrently in GO samples containing H 2 O 2, and indirect photoreaction accounted for ∼70% of GO conversion to CO 2 . Reaction with ·OH causes increases in the concentrations of carboxylic acid groups of photoreacted GO and low-molecular-weight (LMW) species as part of the intermediate photoproducts. Compared to parent GO, intermediate photoproducts exhibited reduced interaction with model cell membranes and altered biomarker responses. Kinetic analysis extrapolating our data to conditions prevalent in sunlit surface waters predicts that initial GO photoreaction is dominated by direct photolysis, while indirect photoreactions involving ·OH determine subsequent conversion of intermediate GO photoproducts to CO 2 . [ABSTRACT FROM AUTHOR]

• A scheduled sampling strategy is introduced to adjust the training procedure of matching network, which accomplishes to learn the ability for one-shot prediction from easy to difficult. • We propose a novel metric to measure the difficulty of training samples, which jointly considers the diversity and similarity among the labels' semantic. Samples with high-difficulty values are more difficult to learn for the matching network. • We conduct extensive experiments on datasets mini -Imagenet, Birds, and Flowers to illustrate the effectiveness and superiority of the proposed method. The experimental results demonstrate that our method consistently outperforms other competitors. Considering human can learn new object successfully from just one sample, one-shot learning, where each visual class just has one labeled sample for training, has attracted more and more attention. In the past years, most researchers achieve one-shot learning by training a matching network to map a small labeled support set and an unlabeled image to its label. The support set is combined by one image with the same label as unlabeled image and few images with other labels generated by random sampling. This random sampling strategy easily generates massive over-easy support sets in which most labels are less relevant to the label of unlabeled image. It leads to the limitation of matching network for one-shot prediction over indistinguishable label sets. For this issue, we propose a novel metric to evaluate the learning difficulty of support set, where this metric jointly considers the semantic diversity and similarity of visual labels. Based on the metric, we introduce a scheduled sampling strategy to train the matching network from easy to difficult. Extensive experimental results on three datasets, including mini-Imagenet, Birds and Flowers, indicate that our method could achieve significant improvements over other previous methods. [ABSTRACT FROM AUTHOR]