Technological and business challenges of smart grids

Growing recognition of the electricity grid modernization to enable new electricity generation and consumption schemes has found articulation in the vision of the Smart Grid platform. The essence of this vision is an autonomous network with two-way electricity power flows and extensive real-time information between the generation nodes, various electricity-dependent appliances and all points in-between. Three major components of the Smart Grids are distributed intelligence, communication technologies, and automated control systems. The aim of this thesis is to recognize the challenges that Smart Grids are facing, while extinguishing the main driving factors for their introduction. The scope of the thesis also covers possible place of electricity Aggregator Company in the current and future electricity markets. Basic functions of an aggregator and possible revenue sources along with demand response feasibility calculations are reviewed within this thesis.

Physical Realization of an Optical Automatic Target Recognition System

In the modern warfare there is an active development of a new trend connected with a robotic warfare. One of the critical elements of robotics warfare systems is an automatic target recognition system, allowing to recognize objects, based on the data received from sensors. This work considers aspects of optical realization of such a system by means of NIR target scanning at fixed wavelengths. An algorithm was designed, an experimental setup was built and samples of various modern gear and apparel materials were tested. For pattern testing the samples of actively arm engaged armies camouflages were chosen. Tests were performed both in clear atmosphere and in the artificial extremely humid and hot atmosphere to simulate field conditions.

Future revenue models: Transformation from textbooks to digital solutions in the educational publishing industry, Case Sanoma Learning

This study was conducted in order to learn how companies’ revenue models will be transformed due to the digitalisation of its products and processes. Because there is still only a limited number of researches focusing solely on revenue models, and particularly on the revenue model change caused by the changes at the business environment, the topic was initially approached through the business model concept, which organises the different value creating operations and resources at a company in order to create profitable revenue streams. This was used as the base for constructing the theoretical framework for this study, used to collect and analyse the information. The empirical section is based on a qualitative study approach and multiple-case analysis of companies operating in learning materials publishing industry. Their operations are compared with companies operating in other industries, which have undergone comparable transformation, in order to recognise either similarities or contrasts between the cases. The sources of evidence are a literature review to find the essential dimensions researched earlier, and interviews 29 of managers and executives at 17 organisations representing six industries. Based onto the earlier literature and the empirical findings of this study, the change of the revenue model is linked with the change of the other dimen-sions of the business model. When one dimension will be altered, as well the other should be adjusted accordingly. At the case companies the transformation is observed as the utilisation of several revenue models simultaneously and the revenue creation processes becoming more complex.

Is professional recognition in plastic surgery related to activity in research

OBJECTIVE: To evaluate the relation of medical research, with the participation of prominent plastic surgeon in Congress.METHODS: We reviewed the scientific programs of the last 3 Brazilian Congress of Surgery, were selected 21 Brazilian plástic surgeons invited to serve as panelists or speakers in roundtable sessions in the last 3 congresses (Group 1). We randomly selected and paired by other members (associates) of the Brazilian Society of Plastic Surgery, with no participation in congress as speaker (Group 2). We conducted a search for articles published in journals indexed in Medline, Lilacs and SciELO for all doctors selected during the entire academic career and the last 5 years from March 2007 until March 2012. We assessed the research activity through the simple counting of the number of publications in indexed journals for each professional. The number of publications groups was compared.RESULTS: articles produced throughout career: Group 1- 639 articles (average of 30.42 items each). Group 2- 79 articles (mean 3.95 articles each). Difference between medias: p <0.001.CONCLUSION: The results demonstrate that the Brazilian Society of Plastic Surgery seeking professionals with a greater number of publications and journals of higher impact. This approach encourages new members to pursue a higher qualification, and give security to congressmen, they can rely on the existence of a technical criterion in the choice of speakers.

Evaluation of Bubble Formation and Break Up in Suppression Pools by Using Pattern Recognition Methods

During a possible loss of coolant accident in BWRs, a large amount of steam will be released from the reactor pressure vessel to the suppression pool. Steam will be condensed into the suppression pool causing dynamic and structural loads to the pool. The formation and break up of bubbles can be measured by visual observation using a suitable pattern recognition algorithm. The aim of this study was to improve the preliminary pattern recognition algorithm, developed by Vesa Tanskanen in his doctoral dissertation, by using MATLAB. Video material from the PPOOLEX test facility, recorded during thermal stratification and mixing experiments, was used as a reference in the development of the algorithm. The developed algorithm consists of two parts: the pattern recognition of the bubbles and the analysis of recognized bubble images. The bubble recognition works well, but some errors will appear due to the complex structure of the pool. The results of the image analysis were reasonable. The volume and the surface area of the bubbles were not evaluated. Chugging frequencies calculated by using FFT fitted well into the results of oscillation frequencies measured in the experiments. The pattern recognition algorithm works in the conditions it is designed for. If the measurement configuration will be changed, some modifications have to be done. Numerous improvements are proposed for the future 3D equipment.

Additional revenue opportunities in pulp mills and their impacts on the kraft process

Although the concept of multi-products biorefinery provides an opportunity to meet the future demands for biofuels, biomaterials or chemicals, it is not assured that its implementation would improve the profitability of kraft pulp mills. The attractiveness will depend on several factors such as mill age and location, government incentives, economy of scale, end user requirements, and how much value can be added to the new products. In addition, the effective integration of alternative technologies is not straightforward and has to be carefully studied. In this work, detailed balances were performed to evaluate possible impacts that lignin removal, hemicelluloses recovery prior to pulping, torrefaction and pyrolysis of wood residues cause on the conventional mill operation. The development of mill balances was based on theoretical fundamentals, practical experience, literature review, personal communication with technology suppliers and analysis of mill process data. Hemicelluloses recovery through pre-hydrolysis of chips leads to impacts in several stages of the kraft process. Effects can be observed on the pulping process, wood consumption, black liquor properties and, inevitably, on the pulp quality. When lignin is removed from black liquor, it will affect mostly the chemical recovery operation and steam generation rate. Since mineral acid is used to precipitate the lignin, impacts on the mill chemical balance are also expected. A great advantage of processing the wood residues for additional income results from the fact that the pulping process, pulp quality and sales are not harmfully affected. For pulp mills interested in implementing the concept of multi-products biorefinery, this work has indicated possible impacts to be considered in a technical feasibility study.

MHC-restricted antigen presentation and recognition: constraints on gene, recombinant and peptide vaccines in humans

The target of any immunization is to activate and expand lymphocyte clones with the desired recognition specificity and the necessary effector functions. In gene, recombinant and peptide vaccines, the immunogen is a single protein or a small assembly of epitopes from antigenic proteins. Since most immune responses against protein and peptide antigens are T-cell dependent, the molecular target of such vaccines is to generate at least 50-100 complexes between MHC molecule and the antigenic peptide per antigen-presenting cell, sensitizing a T cell population of appropriate clonal size and effector characteristics. Thus, the immunobiology of antigen recognition by T cells must be taken into account when designing new generation peptide- or gene-based vaccines. Since T cell recognition is MHC-restricted, and given the wide polymorphism of the different MHC molecules, distinct epitopes may be recognized by different individuals in the population. Therefore, the issue of whether immunization will be effective in inducing a protective immune response, covering the entire target population, becomes an important question. Many pathogens have evolved molecular mechanisms to escape recognition by the immune system by variation of antigenic protein sequences. In this short review, we will discuss the several concepts related to selection of amino acid sequences to be included in DNA and peptide vaccines.

Evidence for the expression of native Mycobacterium tuberculosis phospholipase C: recognition by immune sera and detection of promoter activity

The genome of Mycobacterium tuberculosis H37Rv contains three contiguous genes (plc-a, plc-b and plc-c) which are similar to the Pseudomonas aeruginosa phospholipase C (PLC) genes. Expression of mycobacterial PLC-a and PLC-b in E. coli and M. smegmatis has been reported, whereas expression of the native proteins in M. tuberculosis H37Rv has not been demonstrated. The objective of the present study was to demonstrate that native PLC-a is expressed in M. tuberculosis H37Rv. Sera from mice immunized with recombinant PLC-a expressed in E. coli were used in immunoblots to evaluate PLC-a expression. The immune serum recognized a 49-kDa protein in immunoblots against M. tuberculosis extracts. No bands were visible in M. tuberculosis culture supernatants or extracts from M. avium, M. bovis and M. smegmatis. A 550-bp DNA fragment upstream of plc-a was cloned in the pJEM12 vector and the existence of a functional promoter was evaluated by detection of ß-galactosidase activity. ß-Galactosidase activity was detected in M. smegmatis transformed with recombinant pJEM12 grown in vitro and inside macrophages. The putative promoter was active both in vitro and in vivo, suggesting that expression is constitutive. In conclusion, expression of non-secreted native PLC-a was demonstrated in M. tuberculosis.

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

Automatic recognition of fish from video sequences

The problem of automatic recognition of the fish from the video sequences is discussed in this Master’s Thesis. This is a very urgent issue for many organizations engaged in fish farming in Finland and Russia because the process of automation control and counting of individual species is turning point in the industry. The difficulties and the specific features of the problem have been identified in order to find a solution and propose some recommendations for the components of the automated fish recognition system. Methods such as background subtraction, Kalman filtering and Viola-Jones method were implemented during this work for detection, tracking and estimation of fish parameters. Both the results of the experiments and the choice of the appropriate methods strongly depend on the quality and the type of a video which is used as an input data. Practical experiments have demonstrated that not all methods can produce good results for real data, whereas on synthetic data they operate satisfactorily.

A carbohydrate-based mechanism of species recognition in sea urchin fertilization

In the present review, we describe a systematic study of the sulfated polysaccharides from marine invertebrates, which led to the discovery of a carbohydrate-based mechanism of sperm-egg recognition during sea urchin fertilization. We have described unique polymers present in these organisms, especially sulfated fucose-rich compounds found in the egg jelly coat of sea urchins. The polysaccharides have simple, linear structures consisting of repeating units of oligosaccharides. They differ among the various species of sea urchins in specific patterns of sulfation and/or position of the glycosidic linkage within their repeating units. These polysaccharides show species specificity in inducing the acrosome reaction in sea urchin sperm, providing a clear-cut example of a signal transduction event regulated by sulfated polysaccharides. This distinct carbohydrate-mediated mechanism of sperm-egg recognition coexists with the bindin-protein system. Possibly, the genes involved in the biosynthesis of these sulfated fucans did not evolve in concordance with evolutionary distance but underwent a dramatic change near the tip of the Strongylocentrotid tree. Overall, we established a direct causal link between the molecular structure of a sulfated polysaccharide and a cellular physiological event - the induction of the sperm acrosome reaction in sea urchins. Small structural changes modulate an entire system of sperm-egg recognition and species-specific fertilization in sea urchins. We demonstrated that sulfated polysaccharides - in addition to their known function in cell proliferation, development, coagulation, and viral infection - mediate fertilization, and respond to evolutionary mechanisms that lead to species diversity.

Serotonergic modulation of face-emotion recognition

Facial expressions of basic emotions have been widely used to investigate the neural substrates of emotion processing, but little is known about the exact meaning of subjective changes provoked by perceiving facial expressions. Our assumption was that fearful faces would be related to the processing of potential threats, whereas angry faces would be related to the processing of proximal threats. Experimental studies have suggested that serotonin modulates the brain processes underlying defensive responses to environmental threats, facilitating risk assessment behavior elicited by potential threats and inhibiting fight or flight responses to proximal threats. In order to test these predictions about the relationship between fearful and angry faces and defensive behaviors, we carried out a review of the literature about the effects of pharmacological probes that affect 5-HT-mediated neurotransmission on the perception of emotional faces. The hypothesis that angry faces would be processed as a proximal threat and that, as a consequence, their recognition would be impaired by an increase in 5-HT function was not supported by the results reviewed. In contrast, most of the studies that evaluated the behavioral effects of serotonin challenges showed that increased 5-HT neurotransmission facilitates the recognition of fearful faces, whereas its decrease impairs the same performance. These results agree with the hypothesis that fearful faces are processed as potential threats and that 5-HT enhances this brain processing.

A novel polar-based human face recognition computational model

Motivated by a recently proposed biologically inspired face recognition approach, we investigated the relation between human behavior and a computational model based on Fourier-Bessel (FB) spatial patterns. We measured human recognition performance of FB filtered face images using an 8-alternative forced-choice method. Test stimuli were generated by converting the images from the spatial to the FB domain, filtering the resulting coefficients with a band-pass filter, and finally taking the inverse FB transformation of the filtered coefficients. The performance of the computational models was tested using a simulation of the psychophysical experiment. In the FB model, face images were first filtered by simulated V1- type neurons and later analyzed globally for their content of FB components. In general, there was a higher human contrast sensitivity to radially than to angularly filtered images, but both functions peaked at the 11.3-16 frequency interval. The FB-based model presented similar behavior with regard to peak position and relative sensitivity, but had a wider frequency band width and a narrower response range. The response pattern of two alternative models, based on local FB analysis and on raw luminance, strongly diverged from the human behavior patterns. These results suggest that human performance can be constrained by the type of information conveyed by polar patterns, and consequently that humans might use FB-like spatial patterns in face processing.

Long-term social recognition memory in adult male rats: factor analysis of the social and non-social behaviors

A modified version of the intruder-resident paradigm was used to investigate if social recognition memory lasts at least 24 h. One hundred and forty-six adult male Wistar rats were used. Independent groups of rats were exposed to an intruder for 0.083, 0.5, 2, 24, or 168 h and tested 24 h after the first encounter with the familiar or a different conspecific. Factor analysis was employed to identify associations between behaviors and treatments. Resident rats exhibited a 24-h social recognition memory, as indicated by a 3- to 5-fold decrease in social behaviors in the second encounter with the same conspecific compared to those observed for a different conspecific, when the duration of the first encounter was 2 h or longer. It was possible to distinguish between two different categories of social behaviors and their expression depended on the duration of the first encounter. Sniffing the anogenital area (49.9% of the social behaviors), sniffing the body (17.9%), sniffing the head (3%), and following the conspecific (3.1%), exhibited mostly by resident rats, characterized social investigation and revealed long-term social recognition memory. However, dominance (23.8%) and mild aggression (2.3%), exhibited by both resident and intruders, characterized social agonistic behaviors and were not affected by memory. Differently, sniffing the environment (76.8% of the non-social behaviors) and rearing (14.3%), both exhibited mostly by adult intruder rats, characterized non-social behaviors. Together, these results show that social recognition memory in rats may last at least 24 h after a 2-h or longer exposure to the conspecific.

Interaction between affordance and handedness recognition: a chronometric study

The visualization of tools and manipulable objects activates motor-related areas in the cortex, facilitating possible actions toward them. This pattern of activity may underlie the phenomenon of object affordance. Some cortical motor neurons are also covertly activated during the recognition of body parts such as hands. One hypothesis is that different subpopulations of motor neurons in the frontal cortex are activated in each motor program; for example, canonical neurons in the premotor cortex are responsible for the affordance of visual objects, while mirror neurons support motor imagery triggered during handedness recognition. However, the question remains whether these subpopulations work independently. This hypothesis can be tested with a manual reaction time (MRT) task with a priming paradigm to evaluate whether the view of a manipulable object interferes with the motor imagery of the subject's hand. The MRT provides a measure of the course of information processing in the brain and allows indirect evaluation of cognitive processes. Our results suggest that canonical and mirror neurons work together to create a motor plan involving hand movements to facilitate successful object manipulation.