Stalagmite water content as a proxy for drip water supply in tropical and subtropical areas

In this pilot study water was extracted from samples of two Holocene stalagmites from Socotra Island, Yemen, and one Eemian stalagmite from southern continental Yemen. The amount of water extracted per unit mass of stalagmite rock, termed "water yield" hereafter, serves as a measure of its total water content. Based on direct correlation plots of water yields and δ18Ocalcite and on regime shift analyses, we demonstrate that for the studied stalagmites the water yield records vary systematically with the corresponding oxygen isotopic compositions of the calcite (δ18Ocalcite). Within each stalagmite lower δ18Ocalcite values are accompanied by lower water yields and vice versa. The δ18Ocalcite records of the studied stalagmites have previously been interpreted to predominantly reflect the amount of rainfall in the area; thus, water yields can be linked to drip water supply. Higher, and therefore more continuous drip water supply caused by higher rainfall rates, supports homogeneous deposition of calcite with low porosity and therefore a small fraction of water-filled inclusions, resulting in low water yields of the respective samples. A reduction of drip water supply fosters irregular growth of calcite with higher porosity, leading to an increase of the fraction of water-filled inclusions and thus higher water yields. The results are consistent with the literature on stalagmite growth and supported by optical inspection of thin sections of our samples. We propose that for a stalagmite from a dry tropical or subtropical area, its water yield record represents a novel paleo-climate proxy recording changes in drip water supply, which can in turn be interpreted in terms of associated rainfall rates.

Automated artifact removal from the electroencephalogram; a comparative study

Contamination of the electroencephalogram (EEG) by artifacts greatly reduces the quality of the recorded signals. There is a need for automated artifact removal methods. However, such methods are rarely evaluated against one another via rigorous criteria, with results often presented based upon visual inspection alone. This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG. Three methods are considered; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction. These are applied to data sets containing mixtures of artifacts. Metrics are devised to measure the performance of each method. The BSS method is seen to be the best approach for artifacts of high signal to noise ratio (SNR). By contrast, MSSA performs well at low SNRs but at the expense of a large number of false positive corrections.

Objective Empirical Mode Decomposition metric

Empirical Mode Decomposition (EMD) is a data driven technique for extraction of oscillatory components from data. Although it has been introduced over 15 years ago, its mathematical foundations are still missing which also implies lack of objective metrics for decomposed set evaluation. Most common technique for assessing results of EMD is their visual inspection, which is very subjective. This article provides objective measures for assessing EMD results based on the original definition of oscillatory components.

Assessment of the food control systems in the Sultanate of Oman

National food control systems are vital tools in governing the safety and quality of food intended for human consumption. This study of the Omani system was conducted to evaluate the effectiveness of the current food controls in place for protecting, in particular, the public health from emerging biological and chemical hazards. In response to this situation, a survey was undertaken within the different food safety authorities in Oman to examine the different elements of the national food control systems in terms of their existing food control management, food legislation, food inspection, food analysis laboratories and information, education and communications. Officials from the different authorities were interviewed and results were captured in prepared questionnaires. Overall examinations of the challenges, strength and weakness of the existing system have been highlighted. The findings of the study indicate significant progress is being made and the creation by the government of a national Centre for Food Safety and Quality is a significant positive step.

High-throughput monitoring of wild bee diversity and abundance via mitogenomics

1. Bee populations and other pollinators face multiple, synergistically acting threats, which have led to population declines, loss of local species richness and pollination services, and extinctions. However, our understanding of the degree, distribution and causes of declines is patchy, in part due to inadequate monitoring systems, with the challenge of taxonomic identification posing a major logistical barrier. Pollinator conservation would benefit from a high-throughput identification pipeline. 2. We show that the metagenomic mining and resequencing of mitochondrial genomes (mitogenomics) can be applied successfully to bulk samples of wild bees. We assembled the mitogenomes of 48 UK bee species and then shotgun-sequenced total DNA extracted from 204 whole bees that had been collected in 10 pan-trap samples from farms in England and been identified morphologically to 33 species. Each sample data set was mapped against the 48 reference mitogenomes. 3. The morphological and mitogenomic data sets were highly congruent. Out of 63 total species detections in the morphological data set, the mitogenomic data set made 59 correct detections (93�7% detection rate) and detected six more species (putative false positives). Direct inspection and an analysis with species-specific primers suggested that these putative false positives were most likely due to incorrect morphological IDs. Read frequency significantly predicted species biomass frequency (R2 = 24�9%). Species lists, biomass frequencies, extrapolated species richness and community structure were recovered with less error than in a metabarcoding pipeline. 4. Mitogenomics automates the onerous task of taxonomic identification, even for cryptic species, allowing the tracking of changes in species richness and istributions. A mitogenomic pipeline should thus be able to contain costs, maintain consistently high-quality data over long time series, incorporate retrospective taxonomic revisions and provide an auditable evidence trail. Mitogenomic data sets also provide estimates of species counts within samples and thus have potential for tracking population trajectories.

Comparison of Neck Skin Excision and Whole Carcass Rinse Sampling Methods for Microbiological Evaluation of Broiler Carcasses before and after Immersion Chilling

Sampling protocols for detecting Salmonella on poultry differ among various countries. In the United States, the U.S. Department of Agriculture Food Safety and Inspection Service dictates that whole broiler carcasses should be rinsed with 400 ml of 1% buffered peptone water, whereas in the European Union 25-g samples composed of neck skin from three carcasses are evaluated. The purpose of this study was to evaluate a whole carcass rinse (WCR) and a neck skin excision (NS) procedure for Salmonella and Escherichia coli isolation from the same broiler carcass. Carcasses were obtained from three broiler processing plants. The skin around the neck area was aseptically removed and bagged separately from the carcass, and microbiological analysis was performed. The corresponding carcass was bagged and a WCR sample was evaluated. No significant difference (alpha <= 0.05) in Salmonella prevalence was found between the samples processed by the two methods, but both procedures produced many false-negative Salmonella results. Prechill, 37% (66 carcasses), 28% (50 carcasses), and 51% (91 carcasses) of the 180 carcasses examined were positive for Salmonella by WCR, NS, and both procedures combined, respectively. Postchill, 3% (5 carcasses), 7% (12 carcasses), and 10% (17 carcasses) of the 177 carcasses examined were positive for Salmonella by the WCR, NS, and combination of both procedures, respectively. Prechill, E. coli plus coliform counts were 3.0 and 2.6 log CFU/ml by the WCR and NS methods, respectively. Postchill. E. coli plus coliform counts were 1.7 and 1.4 log CFU/ml by the WCR and NS methods, respectively.

Discovery of two distinct red clumps in NGC 419: a rare snapshot of a cluster at the onset of degeneracy

Colour-magnitude diagrams (CMDs) of the Small Magellanic Cloud (SMC) star cluster NGC 419, derived from Hubble Space Telescope (HST)/Advanced Camera for Surveys (ACS) data, reveal a well-delineated secondary clump located below the classical compact red clump typical of intermediate-age populations. We demonstrate that this feature belongs to the cluster itself, rather than to the underlying SMC field. Then, we use synthetic CMDs to show that it corresponds very well to the secondary clump predicted to appear as a result of He-ignition in stars just massive enough to avoid e(-)-degeneracy settling in their H-exhausted cores. The main red clump instead is made of the slightly less massive stars which passed through e(-) degeneracy and ignited He at the tip of the red giant branch. In other words, NGC 419 is the rare snapshot of a cluster while undergoing the fast transition from classical to degenerate H-exhausted cores. At this particular moment of a cluster`s life, the colour distance between the main-sequence turn-off and the red clump(s) depends sensitively on the amount of convective core overshooting, Lambda(c). By coupling measurements of this colour separation with fits to the red clump morphology, we are able to estimate simultaneously the cluster mean age (1.35(-0.04)(+0.11) Gyr) and overshooting efficiency (Lambda(c) = 0.47(-0.04)(+0.14)). Therefore, clusters like NGC 419 may constitute important marks in the age scale of intermediate-age populations. After eye inspection of other CMDs derived from HST/ACS data, we suggest that the same secondary clump may also be present in the Large Magellanic Cloud clusters NGC 1751, 1783, 1806, 1846, 1852 and 1917.

Use of Information Visualization Methods Eliminating Cross Talk in Multiple Sensing Units Investigated for a Light-Addressable Potentiometric Sensor

The integration of nanostructured films containing biomolecules and silicon-based technologies is a promising direction for reaching miniaturized biosensors that exhibit high sensitivity and selectivity. A challenge, however, is to avoid cross talk among sensing units in an array with multiple sensors located on a small area. In this letter, we describe an array of 16 sensing units, of a light-addressable potentiometric sensor (LAPS), which was made with layer-by-Layer (LbL) films of a poly(amidomine) dendrimer (PAMAM) and single-walled carbon nanotubes (SWNTs), coated with a layer of the enzyme penicillinase. A visual inspection of the data from constant-current measurements with liquid samples containing distinct concentrations of penicillin, glucose, or a buffer indicated a possible cross talk between units that contained penicillinase and those that did not. With the use of multidimensional data projection techniques, normally employed in information Visualization methods, we managed to distinguish the results from the modified LAPS, even in cases where the units were adjacent to each other. Furthermore, the plots generated with the interactive document map (IDMAP) projection technique enabled the distinction of the different concentrations of penicillin, from 5 mmol L(-1) down to 0.5 mmol L(-1). Data visualization also confirmed the enhanced performance of the sensing units containing carbon nanotubes, consistent with the analysis of results for LAPS sensors. The use of visual analytics, as with projection methods, may be essential to handle a large amount of data generated in multiple sensor arrays to achieve high performance in miniaturized systems.

Detection of citrus canker in citrus plants using laser induced fluorescence spectroscopy

Citrus canker is a serious disease caused by Xanthomonas citri subsp. citri bacteria, which infects citrus plants (Citrus spp.) leading to a large economic loss in citrus production worldwide. In Brazil citrus canker control is done by an official eradication campaign, therefore early detection of such disease is important to prevent greater economic losses. However, detection is difficult and so far it has been done by visual inspection of each tree. Suspicious leaves from citrus plants in the field are sent to the laboratory to confirm the infection by laboratory analysis, which is a time consuming. Our goal was to develop a new optical technique to detect and diagnose citrus canker in citrus plants with a portable field spectrometer unit. In this paper, we review two experiments on laser induced fluorescence spectroscopy (LIF) applied to detect citrus canker. We also present new data to show that the length of time a leaf has been detached is an important variable in our studies. Our results show that LIF has the potential to be applied to citrus plants.

Objective Detection and Delineation of Oral Neoplasia Using Autofluorescence Imaging

Although the oral cavity is easily accessible to inspection, patients with oral cancer most often present at a late stage, leading to high morbidity and mortality. Autofluorescence imaging has emerged as a promising technology to aid clinicians in screening for oral neoplasia and as an aid to resection, but current approaches rely on subjective interpretation. We present a new method to objectively delineate neoplastic oral mucosa using autofluorescence imaging. Autofluorescence images were obtained from 56 patients with oral lesions and 11 normal volunteers. From these images, 276 measurements from 159 unique regions of interest (ROI) sites corresponding to normal and confirmed neoplastic areas were identified. Data from ROIs in the first 46 subjects were used to develop a simple classification algorithm based on the ratio of red-to-green fluorescence; performance of this algorithm was then validated using data from the ROIs in the last 21 subjects. This algorithm was applied to patient images to create visual disease probability maps across the field of view. Histologic sections of resected tissue were used to validate the disease probability maps. The best discrimination between neoplastic and nonneoplastic areas was obtained at 405 nm excitation; normal tissue could be discriminated from dysplasia and invasive cancer with a 95.9% sensitivity and 96.2% specificity in the training set, and with a 100% sensitivity and 91.4% specificity in the validation set. Disease probability maps qualitatively agreed with both clinical impression and histology. Autofluorescence imaging coupled with objective image analysis provided a sensitive and noninvasive tool for the detection of oral neoplasia.

Machine vision for the automatic classification of images acquired from Non-destructive tests

This project is based on Artificial Intelligence (A.I) and Digital Image processing (I.P) for automatic condition monitoring of sleepers in the railway track. Rail inspection is a very important task in railway maintenance for traffic safety issues and in preventing dangerous situations. Monitoring railway track infrastructure is an important aspect in which the periodical inspection of rail rolling plane is required.Up to the present days the inspection of the railroad is operated manually by trained personnel. A human operator walks along the railway track searching for sleeper anomalies. This monitoring way is not more acceptable for its slowness and subjectivity. Hence, it is desired to automate such intuitive human skills for the development of more robust and reliable testing methods. Images of wooden sleepers have been used as data for my project. The aim of this project is to present a vision based technique for inspecting railway sleepers (wooden planks under the railway track) by automatic interpretation of Non Destructive Test (NDT) data using A.I. techniques in determining the results of inspection.

Harold Pinter: A Night Out : A Study in the Political ConnotationsAnd the Abuse of Power

Harold Pinter’s A Night Out is a significant but rarely produced piece of drama. Therefore, there is very little criticism to support or contradict my argument. The reason why I chose to do my essay on this particular play is to open doors for academic research and to try and make it an equal to its sister plays. I will raise questions and topics to prove the play is worth the readers’ time and effort and that A Night Out is a sharp piece of political theatre. Although at first glance it is a simple enough story, a straightforward tale of the nasty consequences of motherly love when it is pushed to the limit, on deeper inspection, a more far reaching and complex analysis of the abuse of power can be observed. The play offers a variety of themes, including: interpersonal power struggles, failed attempts at communication, antagonistic relationships, the threat of impending or past violence, the struggle for survival or identity, domination and submission, politics, lies and verbal, physical, psychological and sexual abuse. The prevailing theme in the play is the abuse of power: powerful parties oppressing weaker ones, and the results of the oppressed party looking for a vent in someone even weaker than themselves.

Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

ANALYSIS & STUDY OF AI TECHNIQUES FORAUTOMATIC CONDITION MONITORING OFRAILWAY TRACK INFRASTRUCTURE : Artificial Intelligence Techniques

Since the last decade the problem of surface inspection has been receiving great attention from the scientific community, the quality control and the maintenance of products are key points in several industrial applications.The railway associations spent much money to check the railway infrastructure. The railway infrastructure is a particular field in which the periodical surface inspection can help the operator to prevent critical situations. The maintenance and monitoring of this infrastructure is an important aspect for railway association.That is why the surface inspection of railway also makes importance to the railroad authority to investigate track components, identify problems and finding out the way that how to solve these problems. In railway industry, usually the problems find in railway sleepers, overhead, fastener, rail head, switching and crossing and in ballast section as well. In this thesis work, I have reviewed some research papers based on AI techniques together with NDT techniques which are able to collect data from the test object without making any damage. The research works which I have reviewed and demonstrated that by adopting the AI based system, it is almost possible to solve all the problems and this system is very much reliable and efficient for diagnose problems of this transportation domain. I have reviewed solutions provided by different companies based on AI techniques, their products and reviewed some white papers provided by some of those companies. AI based techniques likemachine vision, stereo vision, laser based techniques and neural network are used in most cases to solve the problems which are performed by the railway engineers.The problems in railway handled by the AI based techniques performed by NDT approach which is a very broad, interdisciplinary field that plays a critical role in assuring that structural components and systems perform their function in a reliable and cost effective fashion. The NDT approach ensures the uniformity, quality and serviceability of materials without causing any damage of that materials is being tested. This testing methods use some way to test product like, Visual and Optical testing, Radiography, Magnetic particle testing, Ultrasonic testing, Penetrate testing, electro mechanic testing and acoustic emission testing etc. The inspection procedure has done periodically because of better maintenance. This inspection procedure done by the railway engineers manually with the aid of AI based techniques.The main idea of thesis work is to demonstrate how the problems can be reduced of thistransportation area based on the works done by different researchers and companies. And I have also provided some ideas and comments according to those works and trying to provide some proposal to use better inspection method where it is needed.The scope of this thesis work is automatic interpretation of data from NDT, with the goal of detecting flaws accurately and efficiently. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in this area. Another scope is to provide an insight into possible research methods concerning railway sleeper, fastener, ballast and overhead inspection by automatic interpretation of data.In this thesis work, I have discussed about problems which are arise in railway sleepers,fastener, and overhead and ballasted track. For this reason I have reviewed some research papers related with these areas and demonstrated how their systems works and the results of those systems. After all the demonstrations were taking place of the advantages of using AI techniques in contrast with those manual systems exist previously.This work aims to summarize the findings of a large number of research papers deploying artificial intelligence (AI) techniques for the automatic interpretation of data from nondestructive testing (NDT). Problems in rail transport domain are mainly discussed in this work. The overall work of this paper goes to the inspection of railway sleepers, fastener, ballast and overhead.