From traditional resource to global commodities: a comparison of Rhodiola species using NMR spectroscopy - metabolomics and HPTLC

The fast developing international trade of products based on traditional knowledge and their value chains has become an important aspect of the ethnopharmacological debate. The structure and diversity of value chains and their impact on the phytochemical composition of herbal medicinal products has been overlooked in the debate about quality problems in transnational trade. Different government policies and regulations governing trade in herbal medicinal products impact on such value chains. Medicinal Rhodiola species, including Rhodiola rosea L. and Rhodiola crenulata (Hook.f. & Thomson) H.Ohba, have been used widely in Europe and Asia as traditional herbal medicines with numerous claims for their therapeutic effects. Faced with resource depletion and environment destruction, R. rosea and R. crenulata are becoming endangered, making them more economically valuable to collectors and middlemen, and also increasing the risk of adulteration and low quality. We compare the phytochemical differences among Rhodiola raw materials available on the market to provide a practical method for Rhodiola authentication and the detection of potential adulterant compounds. Samples were collected from Europe and Asia and nuclear magnetic resonance spectroscopy coupled with multivariate analysis software and high performance thin layer chromatography techniques were used to analyse the samples. A method was developed to quantify the amount of adulterant species contained within mixtures. We compared the phytochemical composition of collected Rhodiola samples to authenticated samples. Rosavin and rosarin were mainly present in R. rosea whereas crenulatin was only present in R. crenulata. 30% of the Rhodiola samples purchased from the Chinese market were adulterated by other Rhodiola spp. Moreover, 7 % of the raw-material samples were not labelled satifactorily. The utilisation of both 1H-NMR and HPTLC methods provided an integrated analysis of the phytochemical differences and novel identification method for R. rosea and R. crenulata. Using 1H-NMR spectroscopy it was possible to quantify the presence of R. crenulata in admixtures with R. rosea. This quantitative technique could be used in the future to assess a variety of herbal drugs and products. This project also highlights the need to further study the links between producers and consumers in national and trans-national trade.

Improving innovation and performance of Social Enterprises: Knowing what they know

Social Enterprises (SEs) are normally micro and small businesses that trade to tackle social problems, and to improve communities, people’s life chances, and the environment. Thus, their importance to society and economies is increasing. However, there is still a need for more understanding of how these organisations operate, perform, innovate and scale-up. This knowledge is crucial to design and provide accurate strategies to enhance the sector and increase its impact and coverage. Obtaining this understanding is the main driver of this paper, which follows the theoretical lens of the Knowledge-based View (KBV) theory to develop and assess empirically a novel model for knowledge management capabilities (KMCs) development that improves performance of SEs. The empirical assessment consisted of a quantitative study with 432 owners and senior members of SEs in UK, underpinned by 21 interviews. The findings demonstrate how particular organisational characteristics of SEs, the external conditions in which they operate, and informal knowledge management activities, have created overall improvements in their performance of up to 20%, based on a year-to-year comparison, including innovation and creation of social and environmental value. These findings elucidate new perspectives that can contribute not only to SEs and SE supporters, but also to other firms.

Performance Evaluation of a Statistical and a Neural Network Model for Nonrigid Shape-Based Registration

Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets.

Distribution, occurrence and biotoxin composition of the main shellfish toxin producing microalgae within European waters: A comparison of methods of analysis.

Harmful algal blooms (HABs) are a natural global phenomena emerging in severity and extent. Incidents have many economic, ecological and human health impacts. Monitoring and providing early warning of toxic HABs are critical for protecting public health. Current monitoring programmes include measuring the number of toxic phytoplankton cells in the water and biotoxin levels in shellfish tissue. As these efforts are demanding and labour intensive, methods which improve the efficiency are essential. This study compares the utilisation of a multitoxin surface plasmon resonance (multitoxin SPR) biosensor with enzyme-linked immunosorbent assay (ELISA) and analytical methods such as high performance liquid chromatography with fluorescence detection (HPLC-FLD) and liquid chromatography–tandem mass spectrometry (LC–MS/MS) for toxic HAB monitoring efforts in Europe. Seawater samples (n = 256) from European waters, collected 2009–2011, were analysed for biotoxins: saxitoxin and analogues, okadaic acid and dinophysistoxins 1/2 (DTX1/DTX2) and domoic acid responsible for paralytic shellfish poisoning (PSP), diarrheic shellfish poisoning (DSP) and amnesic shellfish poisoning (ASP), respectively. Biotoxins were detected mainly in samples from Spain and Ireland. France and Norway appeared to have the lowest number of toxic samples. Both the multitoxin SPR biosensor and the RNA microarray were more sensitive at detecting toxic HABs than standard light microscopy phytoplankton monitoring. Correlations between each of the detection methods were performed with the overall agreement, based on statistical 2 × 2 comparison tables, between each testing platform ranging between 32% and 74% for all three toxin families illustrating that one individual testing method may not be an ideal solution. An efficient early warning monitoring system for the detection of toxic HABs could therefore be achieved by combining both the multitoxin SPR biosensor and RNA microarray.

Distribution, occurrence and biotoxin composition of the main shellfish toxin producing microalgae within European waters: A comparison of methods of analysis.

Harmful algal blooms (HABs) are a natural global phenomena emerging in severity and extent. Incidents have many economic, ecological and human health impacts. Monitoring and providing early warning of toxic HABs are critical for protecting public health. Current monitoring programmes include measuring the number of toxic phytoplankton cells in the water and biotoxin levels in shellfish tissue. As these efforts are demanding and labour intensive, methods which improve the efficiency are essential. This study compares the utilisation of a multitoxin surface plasmon resonance (multitoxin SPR) biosensor with enzyme-linked immunosorbent assay (ELISA) and analytical methods such as high performance liquid chromatography with fluorescence detection (HPLC-FLD) and liquid chromatography–tandem mass spectrometry (LC–MS/MS) for toxic HAB monitoring efforts in Europe. Seawater samples (n = 256) from European waters, collected 2009–2011, were analysed for biotoxins: saxitoxin and analogues, okadaic acid and dinophysistoxins 1/2 (DTX1/DTX2) and domoic acid responsible for paralytic shellfish poisoning (PSP), diarrheic shellfish poisoning (DSP) and amnesic shellfish poisoning (ASP), respectively. Biotoxins were detected mainly in samples from Spain and Ireland. France and Norway appeared to have the lowest number of toxic samples. Both the multitoxin SPR biosensor and the RNA microarray were more sensitive at detecting toxic HABs than standard light microscopy phytoplankton monitoring. Correlations between each of the detection methods were performed with the overall agreement, based on statistical 2 × 2 comparison tables, between each testing platform ranging between 32% and 74% for all three toxin families illustrating that one individual testing method may not be an ideal solution. An efficient early warning monitoring system for the detection of toxic HABs could therefore be achieved by combining both the multitoxin SPR biosensor and RNA microarray.

Underway spectrophotometry along the Atlantic Meridional Transect reveals high performance in satellite chlorophyll retrievals

To evaluate the performance of ocean-colour retrievals of total chlorophyll-a concentration requires direct comparison with concomitant and co-located in situ data. For global comparisons, these in situ match-ups should be ideally representative of the distribution of total chlorophyll-a concentration in the global ocean. The oligotrophic gyres constitute the majority of oceanic water, yet are under-sampled due to their inaccessibility and under-represented in global in situ databases. The Atlantic Meridional Transect (AMT) is one of only a few programmes that consistently sample oligotrophic waters. In this paper, we used a spectrophotometer on two AMT cruises (AMT19 and AMT22) to continuously measure absorption by particles in the water of the ship's flow-through system. From these optical data continuous total chlorophyll-a concentrations were estimated with high precision and accuracy along each cruise and used to evaluate the performance of ocean-colour algorithms. We conducted the evaluation using level 3 binned ocean-colour products, and used the high spatial and temporal resolution of the underway system to maximise the number of match-ups on each cruise. Statistical comparisons show a significant improvement in the performance of satellite chlorophyll algorithms over previous studies, with root mean square errors on average less than half (~ 0.16 in log10 space) that reported previously using global datasets (~ 0.34 in log10 space). This improved performance is likely due to the use of continuous absorption-based chlorophyll estimates, that are highly accurate, sample spatial scales more comparable with satellite pixels, and minimise human errors. Previous comparisons might have reported higher errors due to regional biases in datasets and methodological inconsistencies between investigators. Furthermore, our comparison showed an underestimate in satellite chlorophyll at low concentrations in 2012 (AMT22), likely due to a small bias in satellite remote-sensing reflectance data. Our results highlight the benefits of using underway spectrophotometric systems for evaluating satellite ocean-colour data and underline the importance of maintaining in situ observatories that sample the oligotrophic gyres.

Underway spectrophotometry along the Atlantic Meridional Transect reveals high performance in satellite chlorophyll retrievals

To evaluate the performance of ocean-colour retrievals of total chlorophyll-a concentration requires direct comparison with concomitant and co-located in situ data. For global comparisons, these in situ match-ups should be ideally representative of the distribution of total chlorophyll-a concentration in the global ocean. The oligotrophic gyres constitute the majority of oceanic water, yet are under-sampled due to their inaccessibility and under-represented in global in situ databases. The Atlantic Meridional Transect (AMT) is one of only a few programmes that consistently sample oligotrophic waters. In this paper, we used a spectrophotometer on two AMT cruises (AMT19 and AMT22) to continuously measure absorption by particles in the water of the ship's flow-through system. From these optical data continuous total chlorophyll-a concentrations were estimated with high precision and accuracy along each cruise and used to evaluate the performance of ocean-colour algorithms. We conducted the evaluation using level 3 binned ocean-colour products, and used the high spatial and temporal resolution of the underway system to maximise the number of match-ups on each cruise. Statistical comparisons show a significant improvement in the performance of satellite chlorophyll algorithms over previous studies, with root mean square errors on average less than half (~ 0.16 in log10 space) that reported previously using global datasets (~ 0.34 in log10 space). This improved performance is likely due to the use of continuous absorption-based chlorophyll estimates, that are highly accurate, sample spatial scales more comparable with satellite pixels, and minimise human errors. Previous comparisons might have reported higher errors due to regional biases in datasets and methodological inconsistencies between investigators. Furthermore, our comparison showed an underestimate in satellite chlorophyll at low concentrations in 2012 (AMT22), likely due to a small bias in satellite remote-sensing reflectance data. Our results highlight the benefits of using underway spectrophotometric systems for evaluating satellite ocean-colour data and underline the importance of maintaining in situ observatories that sample the oligotrophic gyres.

Chloride ingress into marine exposed concrete: A comparison of empirical- and physically- based models

In establishing the reliability of performance-related design methods for concrete – which are relevant for resistance against chloride-induced corrosion - long-term experience of local materials and practices and detailed knowledge of the ambient and local micro-climate are critical. Furthermore, in the development of analytical models for performance-based design, calibration against test data representative of actual conditions in practice is required. To this end, the current study presents results from full-scale, concrete pier-stems under long-term exposure to a marine environment with work focussing on XS2 (below mid-tide level) in which the concrete is regarded as fully saturated and XS3 (tidal, splash and spray) in which the concrete is in an unsaturated condition. These exposures represent zones where concrete structures are most susceptible to ionic ingress and deterioration. Chloride profiles and chloride transport behaviour are studied using both an empirical model (erfc function) and a physical model (ClinConc). The time dependency of surface chloride concentration (Cs) and apparent diffusivity (Da) were established for the empirical model whereas, in the ClinConc model (originally based on saturated concrete), two new environmental factors were introduced for the XS3 environmental exposure zone. Although the XS3 is considered as one environmental exposure zone according to BS EN 206-1:2013, the work has highlighted that even within this zone, significant changes in chloride ingress are evident. This study aims to update the parameters of both models for predicting the long term transport behaviour of concrete subjected to environmental exposure classes XS2 and XS3.

A comparison of individual cow versus group concentrate allocation strategies on dry matter intake, milk production, tissue changes, and fertility of Holstein-Friesian cows offered a grass silage diet

A diverse range of concentrate allocation strategies are adopted on dairy farms. The objectives of this study were to examine the effects on cow performance [dry matter (DM) intake (DMI), milk yield and composition, body tissue changes, and fertility] of adopting 2 contrasting concentrate allocation strategies over the first 140 d of lactation. Seventy-seven Holstein-Friesian dairy cows were allocated to 1 of 2 concentrate allocation strategies at calving, namely group or individual cow. Cows on the group strategy were offered a mixed ration comprising grass silage and concentrates in a 50:50 ratio on a DM basis. Cows on the individual cow strategy were offered a basal mixed ration comprising grass silage and concentrates (the latter included in the mix to achieve a mean intake of 6 kg/cow per day), which was formulated to meet the cow’s energy requirements for maintenance plus 24 kg of milk/cow per day. Additional concentrates were offered via an out-of-parlor feeding system, with the amount offered adjusted weekly based on each individual cow’s milk yield during the previous week. In addition, all cows received a small quantity of straw in the mixed ration part of the diet (approximately 0.3 kg/cow per day), plus 0.5 kg of concentrate twice daily in the milking parlor. Mean concentrate intakes over the study period were similar with each of the 2 allocation strategies (11.5 and 11.7 kg of DM/cow per day for group and individual cow, respectively), although the pattern of intake with each treatment differed over time. Concentrate allocation strategy had no effect on either milk yield (39.3 and 38.0 kg/d for group and individual cow, respectively), milk composition, or milk constituent yield. The milk yield response curves with each treatment were largely aligned with the concentrate DMI curves. Cows on the individual cow treatment had a greater range of concentrate DMI and milk yields than those on the group treatment. With the exception of a tendency for cows on the individual cow treatment to lose more body weight to nadir than cows on the group treatment, concentrate allocation strategy had little effect on either body weight or body condition score over the experimental period. Cows on the individual cow treatment had a higher pregnancy rate to first and second service and tended to have a higher 100-d in calf rate than cows on the group treatment. This study demonstrates that concentrate allocation strategy had little effect on overall production performance.

Joint Analysis of Multiple Algorithms and Performance Measures

There has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and time complexity). Once one has developed an approach to a problem of interest, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Standard tests used for this purpose are able to consider jointly neither performance measures nor multiple competitors at once. The aim of this paper is to resolve these issues by developing statistical procedures that are able to account for multiple competing measures at the same time and to compare multiple algorithms altogether. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameters of such models, as usually the number of studied cases is very reduced in such comparisons. Data from a comparison among general purpose classifiers is used to show a practical application of our tests.

Comparison of DES predictions of turbulent flow and heat transfer in a 45° ribbed duct

Numerical predictions of the turbulent flow and heat transfer of a stationary duct with square ribs 45° angled to the main flow direction are presented. The rib height to channel hydraulic diameter is 0.1, the rib pitch to rib height is 10. The calculations have been carried out for a bulk Reynolds number of 50,000. The flows generated by ribs are dominated by separating and reattaching shear layers with vortex shedding and secondary flows in the cross-section. The hybrid RANS-LES approach is adopted to simulate such flows at a reasonable computation cost. The capability of the various versions of DES method, depending the RANS model, such as DES-SA, DES-RKE, DES-SST, have been compared and validated against the experiment. The significant effect of RANS model on the accuracy of the DES prediction has been shown. The DES-SST method, which was able to reproduce the correct physics of flow and heat transfer in a ribbed duct showed better performance than others.

Data Mining for Network Intrusion Detection : A comparison of data mining algorithms and an analysis of relevant features for detecting cyber-attacks

Data mining can be defined as the extraction of implicit, previously un-known, and potentially useful information from data. Numerous re-searchers have been developing security technology and exploring new methods to detect cyber-attacks with the DARPA 1998 dataset for Intrusion Detection and the modified versions of this dataset KDDCup99 and NSL-KDD, but until now no one have examined the performance of the Top 10 data mining algorithms selected by experts in data mining. The compared classification learning algorithms in this thesis are: C4.5, CART, k-NN and Naïve Bayes. The performance of these algorithms are compared with accuracy, error rate and average cost on modified versions of NSL-KDD train and test dataset where the instances are classified into normal and four cyber-attack categories: DoS, Probing, R2L and U2R. Additionally the most important features to detect cyber-attacks in all categories and in each category are evaluated with Weka’s Attribute Evaluator and ranked according to Information Gain. The results show that the classification algorithm with best performance on the dataset is the k-NN algorithm. The most important features to detect cyber-attacks are basic features such as the number of seconds of a network connection, the protocol used for the connection, the network service used, normal or error status of the connection and the number of data bytes sent. The most important features to detect DoS, Probing and R2L attacks are basic features and the least important features are content features. Unlike U2R attacks, where the content features are the most important features to detect attacks.

Quantitative comparison of SensibleThings and Microsoft Azure IoT Hub

With the development of the Internet-of-Things, more and more IoT platforms come up with different structures and characteristics. Making balance of their advantages and disadvantages, we should choose the suitable platform in differ- ent scenarios. For this project, I make comparison of a cloud-based centralized platform, Microsoft Azure IoT hub and a fully distributed platform, Sensi- bleThings. Quantitative comparison is made for performance by 2 scenarios, messages sending speed adds up, devices lie in different location. General com- parison is made for security, utilization and the storage. Finally I draw the con- clusion that SensibleThings performs more stable when a lot of messages push- es to the platform. Microsoft Azure has better geographic expansion. For gener- al comparison, Microsoft Azure IoT hub has better security. The requirement of local device for Microsoft Azure IoT hub is lower than SensibleThings. The SensibleThings are open source and free while Microsoft Azure follow the con- cept “pay as you go” with many throttling limitations for different editions. Microsoft is more user-friendly.

Theoretical and Quantitative Comparison of SensibleThings and GSN

This project is aimed at making comparison between current existing Internet- of-Things (IoT) platforms, SensibleThings (ST) and Global Sensors Networks (GSN). Project can be served as a further work of platforms’ investigation. Comparing and learning from each other aim to contribute to the improvement of future platforms development. Detailed comparison is mainly with the respect of platform feature, communication and data present-frequency performance under stress, and platform node scalability performance on one limited device. Study is conducted through developing applications on each platform, and making measuring performance under the same condition in household network environment. So far, all these respects have had results and been concluded. Qualitatively comparing, GSN performs better in the facets of node’s swift development and deployment, data management, node subscription and connection retry mechanism. Whereas, ST is superior in respects of network package encryption, platform reliability, session initializing latency, and degree of developing freedom. In quantitative comparison, nodes on GSN has better data push pressure resistence while ST nodes works with lower session latency. In terms of data present-frequency, ST node can reach higher updating frequency than GSN node. In the aspect of node sclability on one limited device, ST nodes take the advantage in averagely lower latency than GSN node when nodes number is less than 15 on limited device. But due to sharing mechanism of GSN, on one limited device, it's nodes shows more scalable if platform nodes have similar job.

Performance des chantiers ouverts en contexte de mines profondes

Le présent mémoire expose les résultats d’une étude de la performance des chantiers ouverts dans un contexte de mines profondes ayant été effectuée dans le cadre du projet de recherche « Mines profondes : défis d’exploitation et impacts sur la récupération minéralurgique » réalisé par le département de génie des mines, de la métallurgie et des matériaux de l’Université Laval. Les données utilisées dans le cadre de cette étude sont en lien avec la planification et l’exploitation de plus de mille chantiers minés entre 860 et 2 450 m de profondeur à la mine souterraine LaRonde de Mines Agnico Eagle, la plus profonde actuellement de l’hémisphère ouest. On y présente une revue de la littérature qui fait la description des problématiques de dilution et de pertes opérationnelles de la réserve minérale liées à la performance des chantiers ouverts ainsi que de leurs conséquences sur les projets miniers. Une comparaison des performances planifiées et réalisées au site de LaRonde y est présentée, ainsi que les résultats d’analyses statistiques ayant permis de montrer que la variation du ratio des contraintes in situ avec la profondeur est un facteur d’influence significatif de la performance des chantiers. L’effet de ce facteur d’influence y est aussi illustré et validé à l’aide d’une série de modèles numériques. Le mémoire présente également une première approche visant à développer un modèle d’évaluation de la dilution dans les chantiers ouverts qui prend en compte la profondeur de minage. Finalement, afin d’améliorer la précision de l’estimation de la réserve minérale lors de l’évaluation de projets miniers, une méthodologie d’estimation intégrant le modèle d’évaluation proposé est présentée pour les projets dont les contextes géologique, géotechnique et opérationnel sont comparables à ceux du site de LaRonde.