Age determination and clay layer frequencies of sediments of the Black and Red Sea

Paleoenvironmental proxy data for ocean properties, eolian sediment input, and continental rainfall based on high-resolution analyses of sediment cores from the southwestern Black Sea and the northernmost Gulf of Aqaba were used to infer hydroclimatic changes in northern Anatolia and the northern Red Sea region during the last ~7500 years. Pronounced and coherent multicentennial variations in these records reveal patterns that strongly resemble modern temperature and rainfall anomalies related to the Arctic Oscillation/North Atlantic Oscillation (AO/NAO). These patterns suggest a prominent role of AO/NAO-like atmospheric variability during the Holocene beyond interannual to interdecadal timescales, most likely originating from solar output changes.

Effektiv lagerstyrning med AHP och tvådimensionell artikelklassificering : En fallstudie på Permobil AB, Timrå

Costs related to inventory are usually a significant amount of the company’s total assets. Despite this, companies in general don’t pay a lot of interest in it, even if the benefits from effective inventory are obvious when it comes to less tied up capital, increased customer satisfaction and better working environment. Permobil AB, Timrå is in an intense period when it comes to revenue and growth. The production unit is aiming for an increased output of 30 % in the next two years. To make this possible the company has to improve their way to distribute and handle material,The purpose of the study is to provide useful information and concrete proposals for action, so that the company can build a strategy for an effective and sustainable solution when it comes to inventory management. Alternative methods for making forecasts are suggested, in order to reach a more nuanced perception of different articles, and how they should be managed. Analytic Hierarchy Process (AHP) was used in order to give specially selected persons the chance to decide criteria for how the article should be valued. The criteria they agreed about were annual volume value, lead time, frequency rate and purchase price. The other method that was proposed was a two-dimensional model where annual volume value and frequency was the criteria that specified in which class an article should be placed. Both methods resulted in significant changes in comparison to the current solution. For the spare part inventory different forecast methods were tested and compared with the current solution. It turned out that the current forecast method performed worse than both moving average and exponential smoothing with trend. The small sample of ten random articles is not big enough to reject the current solution, but still the result is a reason enough, for the company to control the quality of the forecasts.

Determination of a Mean Solar Radiation Year and of a Typical Meteorological Year for the region of Funchal in the Madeira Island

This paper presents the determination of a mean solar radiation year and of a typical meteorological year for the region of Funchal in the Madeira Island, Portugal. The data set includes hourly mean and extreme values for air temperature, relative humidity and wind speed and hourly mean values for solar global and diffuse radiation for the period 2004-2014, with maximum data coverage of 99.7%. The determination of the mean solar radiation year consisted, in a first step, in the average of all values for each pair hour/day and, in a second step, in the application of a five days centred moving average of hourly values. The determination of the typical meteorological year was based on Finkelstein-Schafer statistics, which allows to obtain a complete year of real measurements through the selection and combination of typical months, preserving the long term averages while still allowing the analysis of short term events. The typical meteorological year validation was carried out through the comparison of the monthly averages for the typical year with the long term monthly averages. The values obtained were very close, so that the typical meteorological year can accurately represent the long term data series. The typical meteorological year can be used in the simulation of renewable energy systems, namely solar energy systems, and for predicting the energy performance of buildings.

Climatic fluctuations and homogenization of Northeast Brazil using precipitation data.

Before using the basic precipitation data in any agroclimatic study to assess the productivity it is important to check the data series for homogeneity. For this purpose data of 105 locations for the period 1912-1981 over northeast Brazil were used. The preliminary study indicate nonhomogeneity in the time series during 1940's at few locations. The amplitude of variation of time series when taken as 10-year moving average show quite different for different regions. It appears that this amplitude is related to time of onset of effective rains in some extent. There is also great diversity in the fluctuations. They present a great regional diversity. Some diversity. Some of the data in the low latitudes indicate presence of four cycles namely 52, 26, 13 & 6.5. years. The 52-year cycle is also evident in the case of onset of southwest Monsoon over a low latitude zone (Kerala Coast) in India. In the case of south Africa the prominent cycles are 60, 30, 15 & 10 similar situation appears to be present in the higher latitudes of northeast Brazil.

Some misspecfication problems in long-memory time series models

Doctor of Philosophy in Mathematics

Off-line and On-line Affective Recognition of a Computer User through A Biosignal Processing Approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment (“relaxation” vs. “stress”) are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the “relaxation” vs. “stress” states.

A multi-model approach to evaluate the role of environmental variability and fishing pressure in sardine fisheries

Understanding the fluctuations in population abundance is a central question in fisheries. Sardine fisheries is of great importance to Portugal and is data-rich and of primary concern to fisheries managers. In Portugal, sub-stocks of Sardina pilchardus (sardine) are found in different regions: the Northwest (IXaCN), Southwest (IXaCS) and the South coast (IXaS-Algarve). Each of these sardine sub-stocks is affected differently by a unique set of climate and ocean conditions, mainly during larval development and recruitment, which will consequently affect sardine fisheries in the short term. Taking this hypothesis into consideration we examined the effects of hydrographic (river discharge), sea surface temperature, wind driven phenomena, upwelling, climatic (North Atlantic Oscillation) and fisheries variables (fishing effort) on S. pilchardus catch rates (landings per unit effort, LPUE, as a proxy for sardine biomass). A 20-year time series (1989-2009) was used, for the different subdivisions of the Portuguese coast (sardine sub-stocks). For the purpose of this analysis a multi-model approach was used, applying different time series models for data fitting (Dynamic Factor Analysis, Generalised Least Squares), forecasting (Autoregressive Integrated Moving Average), as well as Surplus Production stock assessment models. The different models were evaluated, compared and the most important variables explaining changes in LPUE were identified. The type of relationship between catch rates of sardine and environmental variables varied across regional scales due to region-specific recruitment responses. Seasonality plays an important role in sardine variability within the three study regions. In IXaCN autumn (season with minimum spawning activity, larvae and egg concentrations) SST, northerly wind and wind magnitude were negatively related with LPUE. In IXaCS none of the explanatory variables tested was clearly related with LPUE. In IXaS-Algarve (South Portugal) both spring (period when large abundances of larvae are found) northerly wind and wind magnitude were negatively related with LPUE, revealing that environmental effects match with the regional peak in spawning time. Overall, results suggest that management of small, short-lived pelagic species, such as sardine quotas/sustainable yields, should be adapted to a regional scale because of regional environmental variability.

Aplicaciones de los modelos de predicción bio-inspirados en la administración

Las organizaciones y sus entornos son sistemas complejos. Tales sistemas son difíciles de comprender y predecir. Pese a ello, la predicción es una tarea fundamental para la gestión empresarial y para la toma de decisiones que implica siempre un riesgo. Los métodos clásicos de predicción (entre los cuales están: la regresión lineal, la Autoregresive Moving Average y el exponential smoothing) establecen supuestos como la linealidad, la estabilidad para ser matemática y computacionalmente tratables. Por diferentes medios, sin embargo, se han demostrado las limitaciones de tales métodos. Pues bien, en las últimas décadas nuevos métodos de predicción han surgido con el fin de abarcar la complejidad de los sistemas organizacionales y sus entornos, antes que evitarla. Entre ellos, los más promisorios son los métodos de predicción bio-inspirados (ej. redes neuronales, algoritmos genéticos /evolutivos y sistemas inmunes artificiales). Este artículo pretende establecer un estado situacional de las aplicaciones actuales y potenciales de los métodos bio-inspirados de predicción en la administración.

Do Global CO2 Emissions from Fossil-Fuel Consumption Exhibit Long Memory? A Fractional Integration Analysis

In this article we use an autoregressive fractionally integrated moving average approach to measure the degree of fractional integration of aggregate world CO2 emissions and its five components – coal, oil, gas, cement, and gas flaring. We find that all variables are stationary and mean reverting, but exhibit long-term memory. Our results suggest that both coal and oil combustion emissions have the weakest degree of long-range dependence, while emissions from gas and gas flaring have the strongest. With evidence of long memory, we conclude that transitory policy shocks are likely to have long-lasting effects, but not permanent effects. Accordingly, permanent effects on CO2 emissions require a more permanent policy stance. In this context, if one were to rely only on testing for stationarity and non-stationarity, one would likely conclude in favour of non-stationarity, and therefore that even transitory policy shocks

An optimal weighted-average congestion based pricing scheme for enhanced QoS

Pricing is an effective tool to control congestion and achieve quality of service (QoS) provisioning for multiple differentiated levels of service. In this paper, we consider the problem of pricing for congestion control in the case of a network of nodes under a single service class and multiple queues, and present a multi-layered pricing scheme. We propose an algorithm for finding the optimal state dependent price levels for individual queues, at each node. The pricing policy used depends on a weighted average queue length at each node. This helps in reducing frequent price variations and is in the spirit of the random early detection (RED) mechanism used in TCP/IP networks. We observe in our numerical results a considerable improvement in performance using our scheme over that of a recently proposed related scheme in terms of both throughput and delay performance. In particular, our approach exhibits a throughput improvement in the range of 34 to 69 percent in all cases studied (over all routes) over the above scheme.

An optimal weighted-average congestion based pricing scheme for enhanced QoS, , LNCS 4882, 2007

Pricing is an effective tool to control congestion and achieve quality of service (QoS) provisioning for multiple differentiated levels of service. In this paper, we consider the problem of pricing for congestion control in the case of a network of nodes under a single service class and multiple queues, and present a multi-layered pricing scheme. We propose an algorithm for finding the optimal state dependent price levels for individual queues, at each node. The pricing policy used depends on a weighted average queue length at each node. This helps in reducing frequent price variations and is in the spirit of the random early detection (RED) mechanism used in TCP/IP networks. We observe in our numerical results a considerable improvement in performance using our scheme over that of a recently proposed related scheme in terms of both throughput and delay performance. In particular, our approach exhibits a throughput improvement in the range of 34 to 69 percent in all cases studied (over all routes) over the above scheme.

A weighted average-based external clock synchronisation protocol for wireless sensor networks

Wireless Sensor Networks (WSNs) have many application scenarios where external clock synchronisation may be required because a WSN may consist of components which are not connected to each other. In this paper, we first propose a novel weighted average-based internal clock synchronisation (WICS) protocol, which synchronises all the clocks of a WSN with the clock of a reference node periodically. Based on this protocol, we then propose our weighted average-based external clock synchronisation (WECS) protocol. We have analysed the proposed protocols for maximum synchronisation error and shown that it is always upper bounded. Extensive simulation studies of the proposed protocols have been carried out using Castalia simulator. Simulation results validate our above theoretical claim and also show that the proposed protocols perform better in comparison to other protocols in terms of synchronisation accuracy. A prototype implementation of the WICS protocol using a few TelosB motes also validates the above conclusions.

A weighted average based external clock synchronization protocol for wireless sensor networks

Clock synchronization is an extremely important requirement of wireless sensor networks(WSNs). There are many application scenarios such as weather monitoring and forecasting etc. where external clock synchronization may be required because WSN itself may consists of components which are not connected to each other. A usual approach for external clock synchronization in WSNs is to synchronize the clock of a reference node with an external source such as UTC, and the remaining nodes synchronize with the reference node using an internal clock synchronization protocol. In order to provide highly accurate time, both the offset and the drift rate of each clock with respect to reference node are estimated from time to time, and these are used for getting correct time from local clock reading. A problem with this approach is that it is difficult to estimate the offset of a clock with respect to the reference node when drift rate of clocks varies over a period of time. In this paper, we first propose a novel internal clock synchronization protocol based on weighted averaging technique, which synchronizes all the clocks of a WSN to a reference node periodically. We call this protocol weighted average based internal clock synchronization(WICS) protocol. Based on this protocol, we then propose our weighted average based external clock synchronization(WECS) protocol. We have analyzed the proposed protocols for maximum synchronization error and shown that it is always upper bounded. Extensive simulation studies of the proposed protocols have been carried out using Castalia simulator. Simulation results validate our theoretical claim that the maximum synchronization error is always upper bounded and also show that the proposed protocols perform better in comparison to other protocols in terms of synchronization accuracy. A prototype implementation of the proposed internal clock synchronization protocol using a few TelosB motes also validates our claim.

Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

Estimating the effects of environmental exposures using a weighted mean of monitoring stations

The health effects of environmental hazards are often examined using time series of the association between a daily response variable (e.g., death) and a daily level of exposure (e.g., temperature). Exposures are usually the average from a network of stations. This gives each station equal importance, and negates the opportunity for some stations to be better measures of exposure. We used a Bayesian hierarchical model that weighted stations using random variables between zero and one. We compared the weighted estimates to the standard model using data on health outcomes (deaths and hospital admissions) and exposures (air pollution and temperature) in Brisbane, Australia. The improvements in model fit were relatively small, and the estimated health effects of pollution were similar using either the standard or weighted estimates. Spatial weighted exposures would be probably more worthwhile when there is either greater spatial detail in the health outcome, or a greater spatial variation in exposure.