Reduction Measurements for Calculation in Fuzzy Experiment Scheme

The papers is dedicated to the questions of modeling and basing super-resolution measuring- calculating systems in the context of the conception “device + PC = new possibilities”. By the authors of the article the new mathematical method of solution of the multi-criteria optimization problems was developed. The method is based on physic-mathematical formalism of reduction of fuzzy disfigured measurements. It is shown, that determinative part is played by mathematical properties of physical models of the object, which is measured, surroundings, measuring components of measuring-calculating systems and theirs cooperation as well as the developed mathematical method of processing and interpretation of measurements problem solution.

Defense against node compromise in sensor network security

Recent advances in electronic and computer technologies lead to wide-spread deployment of wireless sensor networks (WSNs). WSNs have wide range applications, including military sensing and tracking, environment monitoring, smart environments, etc. Many WSNs have mission-critical tasks, such as military applications. Thus, the security issues in WSNs are kept in the foreground among research areas. Compared with other wireless networks, such as ad hoc, and cellular networks, security in WSNs is more complicated due to the constrained capabilities of sensor nodes and the properties of the deployment, such as large scale, hostile environment, etc. Security issues mainly come from attacks. In general, the attacks in WSNs can be classified as external attacks and internal attacks. In an external attack, the attacking node is not an authorized participant of the sensor network. Cryptography and other security methods can prevent some of external attacks. However, node compromise, the major and unique problem that leads to internal attacks, will eliminate all the efforts to prevent attacks. Knowing the probability of node compromise will help systems to detect and defend against it. Although there are some approaches that can be used to detect and defend against node compromise, few of them have the ability to estimate the probability of node compromise. Hence, we develop basic uniform, basic gradient, intelligent uniform and intelligent gradient models for node compromise distribution in order to adapt to different application environments by using probability theory. These models allow systems to estimate the probability of node compromise. Applying these models in system security designs can improve system security and decrease the overheads nearly in every security area. Moreover, based on these models, we design a novel secure routing algorithm to defend against the routing security issue that comes from the nodes that have already been compromised but have not been detected by the node compromise detecting mechanism. The routing paths in our algorithm detour those nodes which have already been detected as compromised nodes or have larger probabilities of being compromised. Simulation results show that our algorithm is effective to protect routing paths from node compromise whether detected or not.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as ƒ-test is performed during each node's split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

Defense Against Node Compromise in Sensor Network Security

Recent advances in electronic and computer technologies lead to wide-spread deployment of wireless sensor networks (WSNs). WSNs have wide range applications, including military sensing and tracking, environment monitoring, smart environments, etc. Many WSNs have mission-critical tasks, such as military applications. Thus, the security issues in WSNs are kept in the foreground among research areas. Compared with other wireless networks, such as ad hoc, and cellular networks, security in WSNs is more complicated due to the constrained capabilities of sensor nodes and the properties of the deployment, such as large scale, hostile environment, etc. Security issues mainly come from attacks. In general, the attacks in WSNs can be classified as external attacks and internal attacks. In an external attack, the attacking node is not an authorized participant of the sensor network. Cryptography and other security methods can prevent some of external attacks. However, node compromise, the major and unique problem that leads to internal attacks, will eliminate all the efforts to prevent attacks. Knowing the probability of node compromise will help systems to detect and defend against it. Although there are some approaches that can be used to detect and defend against node compromise, few of them have the ability to estimate the probability of node compromise. Hence, we develop basic uniform, basic gradient, intelligent uniform and intelligent gradient models for node compromise distribution in order to adapt to different application environments by using probability theory. These models allow systems to estimate the probability of node compromise. Applying these models in system security designs can improve system security and decrease the overheads nearly in every security area. Moreover, based on these models, we design a novel secure routing algorithm to defend against the routing security issue that comes from the nodes that have already been compromised but have not been detected by the node compromise detecting mechanism. The routing paths in our algorithm detour those nodes which have already been detected as compromised nodes or have larger probabilities of being compromised. Simulation results show that our algorithm is effective to protect routing paths from node compromise whether detected or not.

Seawater carbonate chemistry and growth rate of Emiliania huxleyi in lab experiment

Predicting the impacts of environmental change on marine organisms, food webs, and biogeochemical cycles presently relies almost exclusively on short-term physiological studies, while the possibility of adaptive evolution is often ignored. Here, we assess adaptive evolution in the coccolithophore Emiliania huxleyi, a well-established model species in biological oceanography, in response to ocean acidification. We previously demonstrated that this globally important marine phytoplankton species adapts within 500 generations to elevated CO2. After 750 and 1000 generations, no further fitness increase occurred, and we observed phenotypic convergence between replicate populations. We then exposed adapted populations to two novel environments to investigate whether or not the underlying basis for high CO2-adaptation involves functional genetic divergence, assuming that different novel mutations become apparent via divergent pleiotropic effects. The novel environment "high light" did not reveal such genetic divergence whereas growth in a low-salinity environment revealed strong pleiotropic effects in high CO2 adapted populations, indicating divergent genetic bases for adaptation to high CO2. This suggests that pleiotropy plays an important role in adaptation of natural E. huxleyi populations to ocean acidification. Our study highlights the potential mutual benefits for oceanography and evolutionary biology of using ecologically important marine phytoplankton for microbial evolution experiments.

Seawater carbonate chemistry and sperm swimming speed of the polychaete Galeolaria caespitosa in lab experiment

The rapidity of ocean acidification intensifies selection pressure for resilient phenotypes, particularly during sensitive early life stages. The scope for selection is greater in species with greater within-species variation in responses to changing environments, thus enhancing the potential for adaptation. We investigated among-male variation in sperm swimming responses (percent motility and swimming speeds) of the serpulid polychaete Galeolaria caespitosa to near- (delta pH 0.3) and far-future ocean acidification (delta pH 0.5). Responses of sperm swimming to acidification varied significantly among males and were overall negative. Robust sperm swimming behavior under near-future ocean acidification in some males may ameliorate climate change impacts, if traits associated with robustness are heritable, and thereby enhance the potential for adaptation to far-future conditions. Reduced sperm swimming in the majority of male G. caespitosa may decrease their fertilization success in a high CO2 future ocean. Resultant changes in offspring production could affect recruitment success and population fitness downstream.

Seawater carbonate chemistry and sperm swimming speed, fertilization success of the Australasian sea urchin Heliocidaris erythrogramma in lab experiment

Background: Climate change will lead to intense selection on many organisms, particularly during susceptible early life stages. To date, most studies on the likely biotic effects of climate change have focused on the mean responses of pooled groups of animals. Consequently, the extent to which inter-individual variation mediates different selection responses has not been tested. Investigating this variation is important, since some individuals may be preadapted to future climate scenarios. Methodology/Principal Findings: We examined the effect of CO2-induced pH changes ("ocean acidification") in sperm swimming behaviour on the fertilization success of the Australasian sea urchin Heliocidaris erythrogramma, focusing on the responses of separate individuals and pairs. Acidification significantly decreased the proportion of motile sperm but had no effect on sperm swimming speed. Subsequent fertilization experiments showed strong inter-individual variation in responses to ocean acidification, ranging from a 44% decrease to a 14% increase in fertilization success. This was partly explained by the significant relationship between decreases in percent sperm motility and fertilization success at delta pH = 0.3, but not at delta pH = 0.5. Conclusions and Significance: The effects of ocean acidification on reproductive success varied markedly between individuals. Our results suggest that some individuals will exhibit enhanced fertilization success in acidified oceans, supporting the concept of 'winners' and 'losers' of climate change at an individual level. If these differences are heritable it is likely that ocean acidification will lead to selection against susceptible phenotypes as well as to rapid fixation of alleles that allow reproduction under more acidic conditions. This selection may ameliorate the biotic effects of climate change if taxa have sufficient extant genetic variation upon which selection can act.

Test-Retest Reliability of Resting-State Magnetoencephalography Power in Sensor and Source Space

Several studies have reported changes in spontaneous brain rhythms that could be used asclinical biomarkers or in the evaluation of neuropsychological and drug treatments in longitudinal studies using magnetoencephalography (MEG). There is an increasing necessity to use these measures in early diagnosis and pathology progression; however, there is a lack of studies addressing how reliable they are. Here, we provide the first test-retest reliability estimate of MEG power in resting-state at sensor and source space. In this study, we recorded 3 sessions of resting-state MEG activity from 24 healthy subjects with an interval of a week between each session. Power values were estimated at sensor and source space with beamforming for classical frequency bands: delta (2–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), low beta (13–20 Hz), high beta (20–30 Hz), and gamma (30–45 Hz). Then, test-retest reliability was evaluated using the intraclass correlation coefficient (ICC). We also evaluated the relation between source power and the within-subject variability. In general, ICC of theta, alpha, and low beta power was fairly high (ICC > 0.6) while in delta and gamma power was lower. In source space, fronto-posterior alpha, frontal beta, and medial temporal theta showed the most reliable profiles. Signal-to-noise ratio could be partially responsible for reliability as low signal intensity resulted inhigh within-subject variability, but also the inherent nature of some brain rhythms in resting-state might be driving these reliability patterns. In conclusion, our results described the reliability of MEG power estimates in each frequency band, which could be considered in disease characterization or clinical trials.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as f-test is performed during each node’s split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

Measuring Hydraulic Conductivity to Wilting Point Using Polymer Tensiometers in an Evaporation Experiment

The polymer tensiometer is a novel instrument to measure soil water pressure heads from saturation to permanent wilting conditions. We used tensiometers of this type in an experiment to determine the hydraulic properties of evaporating soil samples in the laboratory. Relative errors in the hydraulic conductivity function in the wet part were high due to the relatively low accuracy of the pressure transducers, resulting in a large uncertainty in the hydraulic gradient and therefore in the calculated hydraulic conductivity. In the dry part, the error related to this accuracy was on the same order of magnitude as the error related to balance accuracy. Therefore, the method can be assumed adequate for measuring soil hydraulic properties except under very wet conditions. In our experiments, relative error and bias increased significantly at pressure heads less negative than -1 m.

Group Rekeying Schemes for Secure Group Communication in Wireless Sensor Networks

Wireless sensor networks are promising solutions for many applications. However, wireless sensor nodes suffer from many constraints such as low computation capability, small memory, limited energy resources, and so on. Grouping is an important technique to localize computation and reduce communication overhead in wireless sensor networks. In this paper, we use grouping to refer to the process of combining a set of sensor nodes with similar properties. We propose two centralized group rekeying (CGK) schemes for secure group communication in sensor networks. The lifetime of a group is divided into three phases, i.e., group formation, group maintenance, and group dissolution. We demonstrate how to set up the group and establish the group key in each phase. Our analysis shows that the proposed two schemes are computationally efficient and secure.

Experiment in Quantization: Atomic Line Spectra

Laboratory exercises that confront students with decisive ouantum ohenomena nrovide valuable motivation for the kudy of quantum m&hanics. The idea that microscopic matter exists in quantized states can be demonstrated with modern versions of historic experiments: atomic line snectra. blackbodv radiation. and resonance potentials. In this experiment, we present a strikingly simple and visual method for determining the wavelength of spectral lines. This experiment not only shows the inadequacy of classical physics, but also indicates the power of optical measurements.

Interaction of corroding iron with bentonite in the ABM1 experiment at Äspö, Sweden: A microscopic approach

Bentonite and iron metals are common materials proposed for use in deep-seated geological repositories for radioactive waste. The inevitable corrosion of iron leads to interaction processes with the clay which may affect the sealing properties of the bentonite backfill. The objective of the present study was to improve our understanding of this process by studying the interface between iron and compacted bentonite in a geological repository-type setting. Samples of MX-80 bentonite samples which had been exposed to an iron source and elevated temperatures (up to 115ºC) for 2.5 y in an in situ experiment (termed ABM1) at the Äspö Hard Rock Laboratory, Sweden, were investigated by microscopic means, including scanning electron microscopy, μ-Raman spectroscopy, spatially resolved X-ray diffraction, and X-ray fluorescence. The corrosion process led to the formation of a ~100 mm thick corrosion layer containing siderite, magnetite, some goethite, and lepidocrocite mixed with the montmorillonitic clay. Most of the corroded Fe occurred within a 10 mm-thick clay layer adjacent to the corrosion layer. An average corrosion depth of the steel of 22–35 μm and an average Fe2+ diffusivity of 1–26×10–13 m2/s were estimated based on the properties of the Fe-enriched clay layer. In that layer, the corrosion-derived Fe occurred predominantly in the clay matrix. The nature of this Fe could not be identified. No indications of clay transformation or newly formed clay phases were found. A slight enrichment of Mg close to the Fe–clay contact was observed. The formation of anhydrite and gypsum, and the dissolution of some SiO2 resulting from the temperature gradient in the in situ test, were also identified. © 2014, Clay Minerals Society. All right reserved.

Land based mesocosm experiment studying the simultaneous impact of warming and acidification on the planktonic food web of the Eastern Mediterranean

A land based mesocosm experiment focusing on the study of the simultaneous impact of warming and acidification on the planktonic food web of the Eastern Mediterranean took place in August-September 2013 at the mesocosm facilities of HCMR in Crete (CRETACOSMOS). Two different pCO2 (present day and predicted for year 2100) were applied in triplicate mesocosms of 3 m**3. This was tested in two different temperatures (ambient seawater T and ambient T plus 3°C). Twelve mesocosms in total were incubated in two large concrete tanks. Temperature was controlled by sophisticated, automated systems. A large variety of chemical, biological and biochemical variables were studied, including salinity, temperature, light and alkalinity measurements, inorganic and organic, particulate and dissolved, nutrient analyses, biological stock (Chla concentration, enumeration and community composition of microbial, phyto- and zooplankton organisms) and rate (primary, bacterial, viral production, copepod egg production, zooplankton grazing, N2 fixation, P uptake) measurements, bacterial DNA extraction and phytoplankton transcriptomics, calcifiers analyses. Twenty three scientists from 6 Institutes and 5 countries participated in this experiment.

A practical approach for outdoors distributed target localization in wireless sensor networks

Wireless sensor networks are posed as the new communication paradigm where the use of small, low-complexity, and low-power devices is preferred over costly centralized systems. The spectra of potential applications of sensor networks is very wide, ranging from monitoring, surveillance, and localization, among others. Localization is a key application in sensor networks and the use of simple, efficient, and distributed algorithms is of paramount practical importance. Combining convex optimization tools with consensus algorithms we propose a distributed localization algorithm for scenarios where received signal strength indicator readings are used. We approach the localization problem by formulating an alternative problem that uses distance estimates locally computed at each node. The formulated problem is solved by a relaxed version using semidefinite relaxation technique. Conditions under which the relaxed problem yields to the same solution as the original problem are given and a distributed consensusbased implementation of the algorithm is proposed based on an augmented Lagrangian approach and primaldual decomposition methods. Although suboptimal, the proposed approach is very suitable for its implementation in real sensor networks, i.e., it is scalable, robust against node failures and requires only local communication among neighboring nodes. Simulation results show that running an additional local search around the found solution can yield performance close to the maximum likelihood estimate.