Adaptive link-weight routing protocol using cross-layer communication for MANET

Routing efficiency is one of the challenges offered by Mobile Ad-hoc Networks (MANETs). This paper proposes a novel routing technique called Adaptive Link-Weight (ALW) routing protocol. ALW adaptively selects an optimum route on the basis of available bandwidth, low delay and long route lifetime. The technique adapts a cross-layer framework where the ALW is integrated with application and physical layer. The proposed design allows applications to convey preferences to the ALW protocol to override the default path selection mechanism. The results confirm improvement over AODV in terms of network load, route discovery time and link reliability

Salad Consumption in Relation to Daily Dietary Intake and Diet Quality among U.S. Adults, 2003-2012

Backgrounds: This study examined salad consumption in relation to daily dietary intake and diet quality among U.S. adults. Methods: Nationally representative sample came from the National Health and Nutrition Examination Survey 2003-2012 waves. Salad consumption was identified through both Food and Nutrient Database for Dietary Studies codes for salad items and combination code for components of and/or additions to salads. First-difference estimator addressed confounding bias from time-invariant unobservables (e.g., eating habits, taste preferences) by using within-individual variations in salad consumption between 2 nonconsecutive 24-hour dietary recalls. Results: Approximately 28.7% of U.S. adults consumed salad on any given day. Among salad consumers, salad consumption occupied 12.5% of daily total energy, 62.8% vegetable, 11.9% fruit, 18.4% fiber, 9.1% sugar, 20.3% total fat, 14.7% saturated fat, 14.9% cholesterol, and 17.7% sodium intake. Compared to no salad consumption on a dietary recall day, salad consumption was associated with increased daily intake of total energy by 461.5 kJ (110.3 kcal), vegetable 85.0 g, fiber 1.0 g, sugar 5.7 g, total fat 10.0 g, saturated fat 1.3 g, cholesterol 18.7 mg, and sodium 216.3 mg. Salad consumption was associated with an increase in the Healthy Eating Index-2010 score by 4.2. Conclusion: Salad consumption is related to better overall diet quality but also higher total energy, sugar, fat, cholesterol, and sodium intake. Interventions that promote salad consumption should provide low-energy-dense, nutrient-rich salad products. Salad consumers should prudently evaluate the caloric and nutrient content of salad in order to make informed and more healthful diet choices

Short communication: Isolation and identification of bacterial pollutants from the Berg and Plankenburg Rivers in the Western Cape, South Africa

Bacterial species present in the Berg and Plankenburg Rivers (Western Cape, South Africa) were isolated from water and biofilm samples and population shifts between sampling sites were phylogenetically identified. Deoxyribonucleic acid (DNA) extraction of representative isolates was performed and amplified using 2 different primer sets. Various Enterobacteriaceae species were present at all of the sites, confirming faecal contamination. Phylogenetic analyses also showed that, in general, Gram-negative micro-organisms dominated at all of the sites sampled in both the Berg and Plankenburg river systems. Pathogens and opportunistic pathogens, such as Pseudomonas aeruginosa, Staphylococcus sp., and Bacillus cereus, were isolated from the Berg River. Similarly, in the Plankenburg River system, Aeromonas sp., Acinetobacter sp., Stenotrophomonas sp. and Yersinia enterocolitica were also isolated. This raises major health concerns as human population densities along both rivers are high, thus resulting in increased human exposure to these organisms

Identification of metal-tolerant organisms isolated from the Plankenburg River, Western Cape, South Africa

The ability of biofilms to resist pollutants makes them advantageous for use in bioremediation. The objective of this investigation was to isolate metal-tolerant micro-organisms from a site along the Plankenburg River. Microbial biofilms cultivated in multi-channelled flow cells were exposed to varying concentrations of aluminium (Al), iron (Fe), copper (Cu), manganese (Mn), nickel (Ni) and zinc (Zn), stained with the BacLightTM viability probe, visualised using epifluorescence microscopy and analysed using ScionImage. Exposure to the highest Al, Fe, Cu and Mn concentrations increased the percentages of dead cells. A difference in live and dead cells after exposure to varying Zn and Ni concentrations was not evident. When exposed to the lowest concentrations, no notable difference could be detected in comparison with the untreated control. Possible metal-tolerant micro-organisms were identified from the exposed flow cells using polymerase chain reaction (PCR) and deoxyribonucleic acid (DNA) sequencing, followed by ClustalX alignment and phylogenetic analysis. Phylogenetic analysis identified a variety of organisms, including Bacillus sp., Pseudomonas sp., Delftia tsuruhatensis strain A90, Kocuria kristinae strain 6J-5b, Comamonas testosteroni WDL7, Stenotrophomonas maltophilia strain 776, Staphylococcus sp. MOLA:313, Micrococcus sp. TPR14, Sphingomonas sp. 8b-1 and Microbacterium sp. PAO-12. Two major clusters could be distinguished based on their Gram-reactions.Keywords: BacLightTM viability probe, biofilms, phylogenetic analysis, river water, ScionImag

Investigation into the metal contamination of the Plankenburg and Diep Rivers, Western Cape, South Africa

Metal contamination in the Plankenburg and Diep Rivers (Western Cape) was investigated over a 12 and 9 month period, respectively. Aluminium (Al), copper (Cu), iron (Fe), lead (Pb), manganese (Mn), nickel (Ni) and zinc (Zn) concentrations were determined using the nitric acid digestion method and analysed by inductively coupled plasma atomic emission spectrometry (ICP-AES). For both rivers the Al and Fe concentrations were higher than that for all the other metals analysed for in sediment and water samples. The highest concentrations recorded in the Plankenburg River were 13.6 mgE.-1 (water . Week 18, Site B) and 15 018 mgEkg-1 (sediment . Week 1, Site C) for Al, and 48 mgE.-1 (water . Week 43, Site A) and 14 363.8 mgEkg-1 (sediment . Week 1, Site A) for Fe. The highest concentrations recorded in the Diep River were 4 mgE.-1 (water . Week 1, Site A) and 19 179 mgEkg-1 (sediment . Week 1, Site C) for Al, and 513 mgE.-1 (water . Week 27, Site A) and 106 379.5 mgEkg-1 (sediment . Week 9, Site C) for Fe. For most of the metals analysed the concentrations were higher than the recommended water quality guidelines as stipulated by the Department of Water Affairs and Forestry, the Canadian Council for the Ministers of the Environment and the accepted eworld averagef. Point sources of pollution could not be conclusively identified, but runoff from both industrial and residential areas could have contributed to the increased concentrations. Metal concentrations should be routinely monitored and the guidelines should be updated and revised based on the current state of the rivers and pollution sources

Investigation into metal contamination of the Berg River, Western Cape, South Africa

A recent decline in water quality of the Berg River, Western Cape, South Africa, has led to the investigation into the degree of metal pollution in the river system. This study was conducted over a period of one year, from May 2004 to May 2005. The nitric acid digestion technique was used to extract metals from water, sediment and biofilm samples collected at various points (Site A . agricultural area, Site B . informal settlement and Site C . Newton pumping station) along the Berg River. Metal concentrations were determined using inductively coupled plasma atomic emission spectrometry (ICP-AES). The highest mean metal concentrations recorded were as follows; water samples, 6 mgE.-1 for Al, 14.6 mgE.-1 for Fe and 18.8 mg..-1 for Mn; sediment samples, 17 448.8 mgEkg-1 for Al and 26 473.3 mgEkg-1 for Fe; biofilm samples, 876.8 mgE.-1 for Al and 1 017.5 mgE.-1 for Fe. The increased availability, or noteworthy incidence of Al and Fe, could be due to the leaching of metals into the river water from waste and household products associated with the informal settlement and the subsequent settling on sediment. No guidelines were available for metals in biofilms. The highest recorded concentrations in water were for Site C (agricultural area). Recorded concentrations in water fluctuated throughout the study period for most of the metals analysed, but Al and Fe were consistently above the recommended guidelines as stipulated by the Department of Water Affairs and Forestry and the Canadian Council of Ministers of the Environment

Deep learning framework for subject-independent emotion detection using wireless signals.

Emotion states recognition using wireless signals is an emerging area of research that has an impact on neuroscientific studies of human behaviour and well-being monitoring. Currently, standoff emotion detection is mostly reliant on the analysis of facial expressions and/or eye movements acquired from optical or video cameras. Meanwhile, although they have been widely accepted for recognizing human emotions from the multimodal data, machine learning approaches have been mostly restricted to subject dependent analyses which lack of generality. In this paper, we report an experimental study which collects heartbeat and breathing signals of 15 participants from radio frequency (RF) reflections off the body followed by novel noise filtering techniques. We propose a novel deep neural network (DNN) architecture based on the fusion of raw RF data and the processed RF signal for classifying and visualising various emotion states. The proposed model achieves high classification accuracy of 71.67% for independent subjects with 0.71, 0.72 and 0.71 precision, recall and F1-score values respectively. We have compared our results with those obtained from five different classical ML algorithms and it is established that deep learning offers a superior performance even with limited amount of raw RF and post processed time-sequence data. The deep learning model has also been validated by comparing our results with those from ECG signals. Our results indicate that using wireless signals for stand-by emotion state detection is a better alternative to other technologies with high accuracy and have much wider applications in future studies of behavioural sciences

Rethinking gradient weights' influence over saliency map estimation

Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural network's prediction. Gradient-based methods are generally faster than other branches of vision interpretability and independent of human guidance. The performance of CAM-like studies depends on the governing model's layer response, and the influences of the gradients. Typical gradient-oriented CAM studies rely on weighted aggregation for saliency map estimation by projecting the gradient maps into single weight values, which may lead to over generalized saliency map. To address this issue, we use a global guidance map to rectify the weighted aggregation operation during saliency estimation, where resultant interpretations are comparatively clean er and instance-specific. We obtain the global guidance map by performing elementwise multiplication between the feature maps and their corresponding gradient maps. To validate our study, we compare the proposed study with eight different saliency visualizers. In addition, we use seven commonly used evaluation metrics for quantitative comparison. The proposed scheme achieves significant improvement over the test images from the ImageNet, MS-COCO 14, and PASCAL VOC 2012 datasets

Denoising single images by feature ensemble revisited

Image denoising is still a challenging issue in many computer vision sub-domains. Recent studies show that significant improvements are made possible in a supervised setting. However, few challenges, such as spatial fidelity and cartoon-like smoothing remain unresolved or decisively overlooked. Our study proposes a simple yet efficient architecture for the denoising problem that addresses the aforementioned issues. The proposed architecture revisits the concept of modular concatenation instead of long and deeper cascaded connections, to recover a cleaner approximation of the given image. We find that different modules can capture versatile representations, and concatenated representation creates a richer subspace for low-level image restoration. The proposed architecture's number of parameters remains smaller than the number for most of the previous networks and still achieves significant improvements over the current state-of-the-art networks