Sustainable sourcing in global supply network: assessment of geographic challenges and management methods Case: Stora Enso

The purpose of this work was to describe and compare sourcing practices and challenges in different geographies, to discuss possible options to advance sustainability of global sourcing, and to provide examples to answer why sourcing driven by sustainability principles is so challenging to implement. The focus was on comparison between Europe & Asia & South-America from the perspective of sustainability adoption. By analyzing sourcing practices of the case company it was possible to describe main differences and challenges of each continent, available sourcing options, supplier relationships and ways to foster positive chance. In this qualitative case study gathered theoretical material was compared to extensive sourcing practices of case company in a vast supplier network. Sourcing specialist were interviewed and information provided by them analyzed in order to see how different research results and theories are reflecting reality and to find answers to proposed research questions.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

Jätteiden kemiallisten ominaisuuksien spektrometriset online-mittausmenetelmät

Tässä diplomityössä käsiteltiin spektrometrisia online-mittausmenetelmiä jätteiden kemiallisten ja fysikaalisten ominaisuuksien määrittämiseksi. Tavoitteena oli selvittää, mitä ominaisuuksia menetelmillä voidaan mitata ja kuinka luotettavia tuloksia mittauksilla saadaan. Diplomityössä suoritettiin kirjallisuuskatsaus, jossa käsiteltiin kolmen spektrometrisen menetelmän soveltuvuutta reaaliaikaisiin jätemittauksiin. Työn empiirisessä osassa FPXRFanalysaattorilla mitattiin neljän eri jätenäytteen alkuainepitoisuuksia. Mittauksen tarkoituksena oli selvittää, mitä alkuaineita menetelmällä voidaan mitata. FPXRF-analysaattorilla saatuja tuloksia verrattiin ICP-MS-menetelmällä saatuihin tuloksiin regressioanalyysin avulla. Työssä todettiin, että FPXRF-analysaattori sopii parhaiten kaliumin, kalsiumin, ja raudan pitoisuuksien määrittämiseen. Lisäksi lyijyn, sinkin, kromin, kloorin, kuparin, kadmiumin, arseenin, fosforin, molybdeenin ja vanadiinin määrittäminen on mahdollista, mutta tarkan pitoisuuden saamiseksi laboratoriomenetelmien käyttö voi olla tarpeen. Tutkituista jätenäytteistä menetelmä soveltui parhaiten tuhkalle ja kompostille niiden fyysisten ominaisuuksien, kuten homogeenisuuden ja kosteuspitoisuuden takia. Biojätteelle menetelmä soveltui huonosti. FPXRF-analysaattorin luotettavuuteen vaikuttaa näytteen kosteuspitoisuus, homogeenisuus, partikkelikoko, mittaustapa ja laitteen kalibrointi. Työssä tarkastelluilla menetelmillä ei voida tällä hetkellä täysin korvata laboratorioanalyyseja. FPXRF-analysaattoria voidaan kuitenkin käyttää kvalitatiiviseen tai semikvantitatiiviseen haitta-aineiden analysointiin, millä voidaan vähentää kalliiden laboratorioanalyysien tarvetta.

Value co-creation in bio-based chemicals value network

The purpose of this Master’s thesis is to study value co-creation in emerging value network. The main objective is to examine how value is co-created in bio-based chemicals value network. The study provides insights to different actors’ perceived value in the value network and enlightens their motivations to commit to the collaborative partnerships with other actors. Empirical study shows that value co-creation is creation of mutual value for both parties of the relationship by combining their non-competing resources to achieve a common goal. Value co-creation happens in interactions, and trust, commitment and information sharing are essential prerequisites for value co-creation. Value co-creation is not only common value creation, but it is also value that emerges for each actor because of the co-operation with the other actor. Even though the case companies define value mainly in economic terms, the other value elements like value of the partnership, knowledge transfer and innovation are more important for value co-creation.

Modelling the colour difference by artificial neural network

This thesis work studies the modelling of the colour difference using artificial neural network. Multilayer percepton (MLP) network is proposed to model CIEDE2000 colour difference formula. MLP is applied to classify colour points in CIE xy chromaticity diagram. In this context, the evaluation was performed using Munsell colour data and MacAdam colour discrimination ellipses. Moreover, in CIE xy chromaticity diagram just noticeable differences (JND) of MacAdam ellipses centres are computed by CIEDE2000, to compare JND of CIEDE2000 and MacAdam ellipses. CIEDE2000 changes the orientation of blue areas in CIE xy chromaticity diagram toward neutral areas, but on the whole it does not totally agree with the MacAdam ellipses. The proposed MLP for both modelling CIEDE2000 and classifying colour points showed good accuracy and achieved acceptable results.

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.

The design of a reflectance spectrometer and its calibration using SrTiO3 /

A new Ultra-High Vacuum (UHV) reflectance spectrometer was successfully designed, making use of a Janis Industries ST-400 sample cryostat, IR Labs bolometer, and Briiker IFS 66 v/S spectrometer. Two of the noteworthy features include an in situ gold evaporator and internal reference path, both of which allow for the experiment to progress with a completely undisturbed sample position. As tested, the system was designed to operate between 4.2 K and 325 K over a frequency range of 60 - 670 cm~^. This frequency range can easily be extended through the addition of appUcable detectors. Tests were performed on SrTiOa, a highly ionic incipient ferroelectric insulator with a well known reflectance. The presence and temperatmre dependence of the lowest frequency "soft" mode were measured, as was the presence of the other two infrared modes. During the structural phase transition from cubic to tetragonal perovskite, the splitting of the second phonon mode was also observed. All of the collected data indicate good agreement with previous measurements, with a minor discrepency between the actual and recorded sample temperatures.

Development and characterization of concentric capillary nebulizer used in inductively coupled plasma analysis /

A simple, low-cost concentric capillary nebulizer (CCN) was developed and evaluated for ICP spectrometry. The CCN could be operated at sample uptake rates of 0.050-1.00 ml min'^ and under oscillating and non-oscillating conditions. Aerosol characteristics for the CCN were studied using a laser Fraunhofter diffraction analyzer. Solvent transport efficiencies and transport rates, detection limits, and short- and long-term stabilities were evaluated for the CCN with a modified cyclonic spray chamber at different sample uptake rates. The Mg II (280.2nm)/l\/lg 1(285.2nm) ratio was used for matrix effect studies. Results were compared to those with conventional nebulizers, a cross-flow nebulizer with a Scott-type spray chamber, a GemCone nebulizer with a cyclonic spray chamber, and a Meinhard TR-30-K3 concentric nebulizer with a cyclonic spray chamber. Transport efficiencies of up to 57% were obtained for the CCN. For the elements tested, short- and long-term precisions and detection limits obtained with the CCN at 0.050-0.500 ml min'^ are similar to, or better than, those obtained on the same instrument using the conventional nebulizers (at 1.0 ml min'^). The depressive and enhancement effects of easily ionizable element Na, sulfuric acid, and dodecylamine surfactant on analyte signals with the CCN are similar to, or better than, those obtained with the conventional nebulizers. However, capillary clog was observed when the sample solution with high dissolved solids was nebulized for more than 40 min. The effects of data acquisition and data processing on detection limits were studied using inductively coupled plasma-atomic emission spectrometry. The study examined the effects of different detection limit approaches, the effects of data integration modes, the effects of regression modes, the effects of the standard concentration range and the number of standards, the effects of sample uptake rate, and the effect of Integration time. All the experiments followed the same protocols. Three detection limit approaches were examined, lUPAC method, the residual standard deviation (RSD), and the signal-to-background ratio and relative standard deviation of the background (SBR-RSDB). The study demonstrated that the different approaches, the integration modes, the regression methods, and the sample uptake rates can have an effect on detection limits. The study also showed that the different approaches give different detection limits and some methods (for example, RSD) are susceptible to the quality of calibration curves. Multicomponents spectral fitting (MSF) gave the best results among these three integration modes, peak height, peak area, and MSF. Weighted least squares method showed the ability to obtain better quality calibration curves. Although an effect of the number of standards on detection limits was not observed, multiple standards are recommended because they provide more reliable calibration curves. An increase of sample uptake rate and integration time could improve detection limits. However, an improvement with increased integration time on detection limits was not observed because the auto integration mode was used.

Evolutionary synthesis of stochastic gene network models using feature-based search spaces

A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic 'if-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.