Kirkkoherrojen osaamisen johtamisen kehittäminen. Case: Lappeenrannan seurakuntayhtymä

Tässä pro gradu -tutkielmassa tarkastellaan osaamisen johtamista Lappeenrannan seurakuntayhtymässä kirkkoherrojen näkökulmasta. Tutkimuksen tavoitteena on selvittää, miten kirkkoherrojen osaamisen johtamista voidaan kehittää. Tutkielmassa tarkastellaan kirkkoherrojen roolia ja tehtäviä sekä käytössä olevia osaamisen kehittämisen menetelmiä. Lisäksi paneudutaan osaamisen johtamisen haasteisiin ja hengellisen työn erityispiirteisiin. Tutkimus toteutettiin kvalitatiivisena tapaustutkimuksena. Tutkimuksen empiirinen aineisto kerättiin haastattelemalla Lappeenrannan seurakuntayhtymän kaikkia viittä kirkkoherraa. Tutkimuksen tulosten perusteella voidaan havaita, että osaamisen johtaminen ei seurakuntayhtymässä ole kovin suunnitelmallista tai pitkäjänteistä. Tulevaisuuden haasteina nähdään etenkin kirkon yhteiskunnallisen aseman muuttuminen ja jäsenmäärän väheneminen. Suurimpana osaamisen johtamiseen liittyvänä haasteena kirkkoherrat kokevat ajan puutteen. Kirkkoherrojen näkemyksissä omasta roolistaan osaamisen johtamisessa korostuvat kokonaisuuksien hallinta, yleisten suuntaviivojen määrittely ja yhteisen suunnan selkiyttäminen. Osaamisen kehittämisen menetelmiä on käytössä monia, mutta pääpaino on keskusteluissa ja palavereissa sekä koulutuksissa. Hengellisen työn erityispiirteinä nähdään kirkon erityinen arvomaailma sekä uskon henkilökohtainen ja intiimi olemus. Osaaminen tulisi seurakuntayhtymässä ottaa tietoiseksi johtamisen kohteeksi. Kirkkoherrat voivat kehittää omaa osaamisen johtamistaan parantamalla tietoisuutta esimiehen eri rooleista ja tehtäväkentistä. Erityisesti yksilöiden oppimisen tukemiseen ja oppimista edistävän ilmapiirin luomiseen tulisi tulevaisuudessa kiinnittää huomiota. Osaamisen kehittämisen menetelmistä suositeltavia ovat etenkin erilaiset työssä oppimisen keinot.

The Nature of Object of Perception and Its Role in the Knowledge Concerning the External World

Questions concerning perception are as old as the field of philosophy itself. Using the first-person perspective as a starting point and philosophical documents, the study examines the relationship between knowledge and perception. The problem is that of how one knows what one immediately perceives. The everyday belief that an object of perception is known to be a material object on grounds of perception is demonstrated as unreliable. It is possible that directly perceived sensible particulars are mind-internal images, shapes, sounds, touches, tastes and smells. According to the appearance/reality distinction, the world of perception is the apparent realm, not the real external world. However, the distinction does not necessarily refute the existence of the external world. We have a causal connection with the external world via mind-internal particulars, and therefore we have indirect knowledge about the external world through perceptual experience. The research especially concerns the reasons for George Berkeley’s claim that material things are mind-dependent ideas that really are perceived. The necessity of a perceiver’s own qualities for perceptual experience, such as mind, consciousness, and the brain, supports the causal theory of perception. Finally, it is asked why mind-internal entities are present when perceiving an object. Perception would not directly discern material objects without the presupposition of extra entities located between a perceiver and the external world. Nevertheless, the results show that perception is not sufficient to know what a perceptual object is, and that the existence of appearances is necessary to know that the external world is being perceived. However, the impossibility of matter does not follow from Berkeley’s theory. The main result of the research is that singular knowledge claims about the external world never refer directly and immediately to the objects of the external world. A perceiver’s own qualities affect how perceptual objects appear in a perceptual situation.

Development of an Ecology-Oriented Software-Defined Networking Framework

ICT contributed to about 0.83 GtCO2 emissions where the 37% comes from the telecoms infrastructures. At the same time, the increasing cost of energy has been hindering the industry in providing more affordable services for the users. One of the sources of these problems is said to be the rigidity of the current network infrastructures which limits innovations in the network. SDN (Software Defined Network) has emerged as one of the prominent solutions with its idea of abstraction, visibility, and programmability in the network. Nevertheless, there are still significant efforts needed to actually utilize it to create a more energy and environmentally friendly network. In this paper, we suggested and developed a platform for developing ecology-related SDN applications. The main approach we take in realizing this goal is by maximizing the abstractions provided by OpenFlow and to expose RESTful interfaces to modules which enable energy saving in the network. While OpenFlow is made to be the standard for SDN protocol, there are still some mechanisms not defined in its specification such as settings related to Quality of Service (QoS). To solve this, we created REST interfaces for setting of QoS in the switches which can maximize network utilization. We also created a module for minimizing the required network resources in delivering packets across the network. This is achieved by utilizing redundant links when it is needed, but disabling them when the load in the network decreases. The usage of multi paths in a network is also evaluated for its benefit in terms of transfer rate improvement and energy savings. Hopefully, the developed framework can be beneficial for developers in creating applications for supporting environmentally friendly network infrastructures.

Generative Part-Based Gabor Object Detector

Object detection is a fundamental task of computer vision that is utilized as a core part in a number of industrial and scientific applications, for example, in robotics, where objects need to be correctly detected and localized prior to being grasped and manipulated. Existing object detectors vary in (i) the amount of supervision they need for training, (ii) the type of a learning method adopted (generative or discriminative) and (iii) the amount of spatial information used in the object model (model-free, using no spatial information in the object model, or model-based, with the explicit spatial model of an object). Although some existing methods report good performance in the detection of certain objects, the results tend to be application specific and no universal method has been found that clearly outperforms all others in all areas. This work proposes a novel generative part-based object detector. The generative learning procedure of the developed method allows learning from positive examples only. The detector is based on finding semantically meaningful parts of the object (i.e. a part detector) that can provide additional information to object location, for example, pose. The object class model, i.e. the appearance of the object parts and their spatial variance, constellation, is explicitly modelled in a fully probabilistic manner. The appearance is based on bio-inspired complex-valued Gabor features that are transformed to part probabilities by an unsupervised Gaussian Mixture Model (GMM). The proposed novel randomized GMM enables learning from only a few training examples. The probabilistic spatial model of the part configurations is constructed with a mixture of 2D Gaussians. The appearance of the parts of the object is learned in an object canonical space that removes geometric variations from the part appearance model. Robustness to pose variations is achieved by object pose quantization, which is more efficient than previously used scale and orientation shifts in the Gabor feature space. Performance of the resulting generative object detector is characterized by high recall with low precision, i.e. the generative detector produces large number of false positive detections. Thus a discriminative classifier is used to prune false positive candidate detections produced by the generative detector improving its precision while keeping high recall. Using only a small number of positive examples, the developed object detector performs comparably to state-of-the-art discriminative methods.

Digitalisaatio ja kuljettajakoulutusyrittäjyys

Digitalisaation myötä myös liikenteestä tulee yhä älykkäämpää. Valtiovalta purkaa sääntelyä ja sallii digitaalisten menetelmien laajempaa käyttöä. Kuljettajakoulutusta pidetään toimialana kuitenkin hyvin konventionaalisena. Diplomityön tarkoituksena on tutkia, mitä digitalisaatio tarkoittaa kuljettajakoulutusyritysten liiketoimintamalleille. Empiiristä aineistoa saatiin teemahaastatteluin ja aineistoa analysoitiin laadullisin menetelmin. Työssä esitellään alan vahvuudet, heikkoudet, mahdollisuudet ja uhat sekä tulevaisuuden skenaariot. Digitalisaatio aiheuttaa merkittäviä muutoksia kuljettajakoulutusalan yrityksille. Auto ei ole enää entisenlainen statussymboli eikä rahan käytön kohde. Digitaaliajan ihmiset eivät aina kaipaa fyysistä liikkumista, kun vielä kivijalkakaupatkin vähenevät. Ajokorttia ei useinkaan koeta välttämättömäksi aikuistumisriitiksi. Uusi teknologia voi kuitenkin radikaalisti parantaa alan yritysten suorituskykyä: palvelut muuttuvat ajasta ja paikasta riippumattomiksi sekä skaalautuviksi. Kuluttajien kannalta digitalisaatio puolestaan parantaa asiakaslähtöisyyttä. Alan liiketoimintamallien kehittymiseen vaikuttaa neljä taustavoimaa: digitalisaatio, perinteet, sääntely ja yrittäjyys. Liiketoimintamalli sisältää opetukselliset ydintoiminnot, sisäiset prosessit, liiketoiminnan tukitoiminnot ja arvoehdotuksen asiakkaalle. Liiketoiminnan kehittäminen vastaamaan digitalisaation vaatimuksia edellyttää proaktiivista innovaatiostrategiaa. Siihen perustuvien innovaatiomenetelmien avulla yritys voi kehittää liiketoimintamalliaan digitalisaation tarjoamien ja tiedon asymmetriasta kumpuavien mahdollisuuksien hyödyntämiseksi.

Adopting the Vaadin Java framework to rebuild VTT’s Web Interfaces and Database Applications

With the growth in new technologies, using online tools have become an everyday lifestyle. It has a greater impact on researchers as the data obtained from various experiments needs to be analyzed and knowledge of programming has become mandatory even for pure biologists. Hence, VTT came up with a new tool, R Executables (REX) which is a web application designed to provide a graphical interface for biological data functions like Image analysis, Gene expression data analysis, plotting, disease and control studies etc., which employs R functions to provide results. REX provides a user interactive application for the biologists to directly enter the values and run the required analysis with a single click. The program processes the given data in the background and prints results rapidly. Due to growth of data and load on server, the interface has gained problems concerning time consumption, poor GUI, data storage issues, security, minimal user interactive experience and crashes with large amount of data. This thesis handles the methods by which these problems were resolved and made REX a better application for the future. The old REX was developed using Python Django and now, a new programming language, Vaadin has been implemented. Vaadin is a Java framework for developing web applications and the programming language is extremely similar to Java with new rich components. Vaadin provides better security, better speed, good and interactive interface. In this thesis, subset functionalities of REX was selected which includes IST bulk plotting and image segmentation and implemented those using Vaadin. A code of 662 lines was programmed by me which included Vaadin as the front-end handler while R language was used for back-end data retrieval, computing and plotting. The application is optimized to allow further functionalities to be migrated with ease from old REX. Future development is focused on including Hight throughput screening functions along with gene expression database handling

Searching for music archives and composition manuscripts from the Viola database

Music archives and composition manuscripts from the Viola database.

Controlling full penetration in MAG welding by the application of infrared thermography and neural network

In this study, an infrared thermography based sensor was studied with regard to usability and the accuracy of sensor data as a weld penetration signal in gas metal arc welding. The object of the study was to evaluate a specific sensor type which measures thermography from solidified weld surface. The purpose of the study was to provide expert data for developing a sensor system in adaptive metal active gas (MAG) welding. Welding experiments with considered process variables and recorded thermal profiles were saved to a database for further analysis. To perform the analysis within a reasonable amount of experiments, the process parameter variables were gradually altered by at least 10 %. Later, the effects of process variables on weld penetration and thermography itself were considered. SFS-EN ISO 5817 standard (2014) was applied for classifying the quality of the experiments. As a final step, a neural network was taught based on the experiments. The experiments show that the studied thermography sensor and the neural network can be used for controlling full penetration though they have minor limitations, which are presented in results and discussion. The results are consistent with previous studies and experiments found in the literature.