<p>The objective of this master thesis was to study the relation between public green open space and health among adolescents in Uppsala municipality, Sweden. The aim of the study was to investigate if there is an association between the access to green open space in the living environment and adolescent´s health, in terms of fysical activity, self- reported health and overweight. The public health questionnaire ”Liv och Hälsa Ung 2009” provided data together with a map over public green space, where the measure taken into account is the proportion of public green space in the living environment. The study includes 3423 adolescents, 12-19 years old. The statistical results indicate that access to public green space in the living environment is genrally not correlated to the health variables studied in this thesis. The author´s interpretation is that other health determinants are more crucial to adolescent´s health than green space, parents educationlevel is sugested as one. Further research is needed to support the findings of this study.</p> / <p>Syftet med föreliggande studie var att undersöka om det finns samband mellan tillgång till grönområden i boendeområdet och ungas hälsa (med avseende på fysisk aktivitet, självskattad hälsa och övervikt) i Uppsala kommun. Studien utgår i från redan befintliga data genom frågeformuläret ”Liv och Hälsa Ung 2009” som riktar sig till elever i årskurs 7 och 9 i högstadiet samt årskurs 2 i gymnasiet i Uppsala län. Studien omfattar 3423 elever i åldern 12-19 år. Med utgångspunkt i enkäten samt med hjälp av en karta över Uppsala kommuns grönområden undersöks måttet andel offentlig grönyta i boendeområden i förhållande till de valda hälsovariablerna. Sammanfattningsvis uppvisar resultatet få uppmätta samband mellan tillgång till grönområden i boendeområdet och de hälsovariabler som studerats. Författarens slutsats är att andra faktorer verkar vara viktigare för ungas hälsa, som exempelvis föräldrarnas utbildningsnivå. Det behövs dock fler studier genomföras för att bekräfta studiens fynd.</p>

The objective of this master thesis was to study the relation between public green open space and health among adolescents in Uppsala municipality, Sweden. The aim of the study was to investigate if there is an association between the access to green open space in the living environment and adolescent´s health, in terms of fysical activity, self- reported health and overweight. The public health questionnaire ”Liv och Hälsa Ung 2009” provided data together with a map over public green space, where the measure taken into account is the proportion of public green space in the living environment. The study includes 3423 adolescents, 12-19 years old. The statistical results indicate that access to public green space in the living environment is genrally not correlated to the health variables studied in this thesis. The author´s interpretation is that other health determinants are more crucial to adolescent´s health than green space, parents educationlevel is sugested as one. Further research is needed to support the findings of this study. / Syftet med föreliggande studie var att undersöka om det finns samband mellan tillgång till grönområden i boendeområdet och ungas hälsa (med avseende på fysisk aktivitet, självskattad hälsa och övervikt) i Uppsala kommun. Studien utgår i från redan befintliga data genom frågeformuläret ”Liv och Hälsa Ung 2009” som riktar sig till elever i årskurs 7 och 9 i högstadiet samt årskurs 2 i gymnasiet i Uppsala län. Studien omfattar 3423 elever i åldern 12-19 år. Med utgångspunkt i enkäten samt med hjälp av en karta över Uppsala kommuns grönområden undersöks måttet andel offentlig grönyta i boendeområden i förhållande till de valda hälsovariablerna. Sammanfattningsvis uppvisar resultatet få uppmätta samband mellan tillgång till grönområden i boendeområdet och de hälsovariabler som studerats. Författarens slutsats är att andra faktorer verkar vara viktigare för ungas hälsa, som exempelvis föräldrarnas utbildningsnivå. Det behövs dock fler studier genomföras för att bekräfta studiens fynd.

This thesis represents a tolerance near set approach to detect similarity between digital images. Two images are considered as sets of perceptual objects and a tolerance relation defines the nearness between objects. Two perceptual objects resemble each other if the difference between their descriptions is smaller than a tolerable level of error. Existing tolerance near set approaches to image similarity consider both images in a single tolerance space and compare the size of tolerance classes. This approach is shown to be sensitive to noise and distortions. In this thesis, a new tolerance-based method is proposed that considers each image in a separate tolerance space and defines the similarity based on differences between histograms of the size of tolerance classes. The main advantage of the proposed method is its lower sensitivity to distortions such as adding noise, darkening or brightening. This advantage has been shown here through a set of experiments.

This thesis represents a tolerance near set approach to detect similarity between digital images. Two images are considered as sets of perceptual objects and a tolerance relation defines the nearness between objects. Two perceptual objects resemble each other if the difference between their descriptions is smaller than a tolerable level of error. Existing tolerance near set approaches to image similarity consider both images in a single tolerance space and compare the size of tolerance classes. This approach is shown to be sensitive to noise and distortions. In this thesis, a new tolerance-based method is proposed that considers each image in a separate tolerance space and defines the similarity based on differences between histograms of the size of tolerance classes. The main advantage of the proposed method is its lower sensitivity to distortions such as adding noise, darkening or brightening. This advantage has been shown here through a set of experiments.

La veille anticipative stratégique et intelligence collective (VASIC) proposée par Lesca est une méthode aidant les entreprises à se mettre à l'écoute de leur environnement pour anticiper des opportunités ou des risques. Cette méthode nécessite la collecte d'informations. Or, avec le développement des technologies de l'information, les salariés font face à une surabondance d'informations. Afin d'aider à pérenniser le dispositif de veille stratégique, il est nécessaire de mettre en place des outils pour gérer la surinformation. Dans cette thèse, nous proposons une mesure de voisinage pour estimer si deux informations sont proches ; nous avons créé un prototype, nommé Alhena, basé sur cette mesure. Nous démontrons les propriétés de notre mesure ainsi que sa pertinence dans le cadre de la veille stratégique. Nous montrons également que le prototype peut servir dans d'autres domaines tels que la littérature, l'informatique et la psychologie. Ce travail est pluridisciplinaire : il aborde des aspects de veille stratégique (en sciences de gestion), de la recherche d'informations, d'informatique linguistique et de mathématiques. Nous nous sommes attachés à partir d'un problème concret en sciences de gestion à proposer un outil qui opérationnalise des techniques informatiques et mathématiques en vue d'une aide à la décision (gain de temps, aide à la lecture,...). / Business environmental scanning and collective intelligence (VASIC) as proposed by Lesca is a method to help companies tune in to their environment to anticipate opportunities or risks. This method requires collecting information, yet with the development of information technology, employees face a glut of information. To help sustain VASIC, it is necessary to develop tools to manage information overload. In this thesis, we propose a nearness measurement to estimate if two pieces of information are similar and we have created a prototype, called Alhena, based on this measurement. We demonstrate the properties of our measurement and its relevance in the context of VASIC. We also show that the prototype can be used in other fields such as literature, computer science and psychology. This work is multidisciplinary as it covers aspects of business environmental scanning (management science), research information, computer linguistics and mathematics. We focus on a concrete problem in management science to provide a tool that operationalizes computational and mathematical techniques with a goal of providing decision making support (time saving, reading assistance, ...).

The focus of this research is on a tolerance space-based approach to image analysis and correspondence. The problem considered in this thesis is one of extracting perceptually relevant information from groups of objects based on their descriptions. Object descriptions are represented by feature vectors containing probe function values in a manner similar to feature extraction in pattern classification theory. The motivation behind this work is the synthesizing of human perception of nearness for improvement of image processing systems. In these systems, the desired output is similar to the output of a human performing the same task. Thus, it is important to have systems that accurately model human perception. Near set theory provides a framework for measuring the similarity of objects based on features that describe them in much the same way that humans perceive the similarity of objects. In this thesis, near set theory is presented and advanced, and work is presented toward a near set approach to performing content-based image retrieval. Furthermore, results are given based on these new techniques and future work is presented. The contributions of this thesis are: the introduction of a nearness measure to determine the degree that near sets resemble each other; a systematic approach to finding tolerance classes, together with proofs demonstrating that the proposed approach will find all tolerance classes on a set of objects; an approach to applying near set theory to images; the application of near set theory to the problem of content-based image retrieval; demonstration that near set theory is well suited to solving problems in which the outcome is similar to that of human perception; two other near set measures, one based on Hausdorff distance, the other based on Hamming distance.

The main contribution of this thesis, is to define similarity measures between two images with the main focus on content-based image retrieval (CBIR). Each image is considered as a set of visual elements that can be described with a set of visual descriptions (features). The similarity between images is then defined as the nearness between sets of elements based on a tolerance and a fuzzy tolerance relation. A tolerance relation is used to describe the approximate nature of the visual perception. A fuzzy tolerance relation is adopted to eliminate the need for a sharp threshold and hence model the gradual changes in perception of similarities. Three real valued similarity measures as well as a fuzzy valued similarity measure are proposed. All of the methods are then used in two CBIR experiments and the results are compared with classical measures of distance (namely, Kantorovich, Hausdorff and Mahalanobis). The results are compared with other published research papers. An important advantage of the proposed methods is shown to be their effectiveness in an unsupervised setting with no prior information. Eighteen different features (based on color, texture and edge) are used in all the experiments. A feature selection algorithm is also used to train the system in choosing a suboptimal set of visual features.

The focus of this research is on a tolerance space-based approach to image analysis and correspondence. The problem considered in this thesis is one of extracting perceptually relevant information from groups of objects based on their descriptions. Object descriptions are represented by feature vectors containing probe function values in a manner similar to feature extraction in pattern classification theory. The motivation behind this work is the synthesizing of human perception of nearness for improvement of image processing systems. In these systems, the desired output is similar to the output of a human performing the same task. Thus, it is important to have systems that accurately model human perception. Near set theory provides a framework for measuring the similarity of objects based on features that describe them in much the same way that humans perceive the similarity of objects. In this thesis, near set theory is presented and advanced, and work is presented toward a near set approach to performing content-based image retrieval. Furthermore, results are given based on these new techniques and future work is presented. The contributions of this thesis are: the introduction of a nearness measure to determine the degree that near sets resemble each other; a systematic approach to finding tolerance classes, together with proofs demonstrating that the proposed approach will find all tolerance classes on a set of objects; an approach to applying near set theory to images; the application of near set theory to the problem of content-based image retrieval; demonstration that near set theory is well suited to solving problems in which the outcome is similar to that of human perception; two other near set measures, one based on Hausdorff distance, the other based on Hamming distance.

The main contribution of this thesis, is to define similarity measures between two images with the main focus on content-based image retrieval (CBIR). Each image is considered as a set of visual elements that can be described with a set of visual descriptions (features). The similarity between images is then defined as the nearness between sets of elements based on a tolerance and a fuzzy tolerance relation. A tolerance relation is used to describe the approximate nature of the visual perception. A fuzzy tolerance relation is adopted to eliminate the need for a sharp threshold and hence model the gradual changes in perception of similarities. Three real valued similarity measures as well as a fuzzy valued similarity measure are proposed. All of the methods are then used in two CBIR experiments and the results are compared with classical measures of distance (namely, Kantorovich, Hausdorff and Mahalanobis). The results are compared with other published research papers. An important advantage of the proposed methods is shown to be their effectiveness in an unsupervised setting with no prior information. Eighteen different features (based on color, texture and edge) are used in all the experiments. A feature selection algorithm is also used to train the system in choosing a suboptimal set of visual features.

Thesis (M.Sc. (Mathematics)) -- University of Limpopo, 2021 / We study balanced filters and balanced z-filters considered by Carlson in [20] and [21] in topological spaces. We consider closed filters which are open-generated and open filters which are closed-generated. We show that a closed filter is open-generated precisely if it is a minimal balanced closed filter and that an open filter is closed-generated precisely when it is a minimal balanced open filter. For a completely regular topological space X, we study balanced z-filters and show that there is a one-to-one correspondence between the nonempty closed sets of βX and the balanced z-filter on X. By dualising closed filters we obtain ideals which then enables us to put some of the results in the context of frames. Dube in [28] has shown that a frame is normal if and only if its closed-generated filters are precisely the stably closed-generated ones. By dualisation we show that a frame is extremally disconnected if and only if its open-generated ideals are precisely the stably open-generated ones. We show that there is one-to-one correspondence between points of βL and the balanced ideals of Coz L. Furthermore we study nearness frames and show that the locally finite nearness frames strictly contain the Pervin nearness frames and the two coincide if the locally finite nearness frames are totally bounded. For perfect extension h : M → L of L, we show that a point p of M is a remote point if and only if Ip = {a ∈ L | h∗(a) ≤ p}. / University of Limpopo and DST - NRF Centre of Excellence in Mathematical and Statistical Sciences (CoE - MaSS)