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  • 51.
    Huq, Asif
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Hartwig, Fredrik
    Dalarna University, School of Technology and Business Studies, Business Administration and Management. Högskolan i Gävle.
    Rudholm, Niklas
    Handelns utredningsinstitut.
    Do Audited Firms Have Lower Cost of Debt?2018Conference paper (Refereed)
  • 52.
    Huq, Asif
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Hartwig, Fredrik
    Dalarna University, School of Technology and Business Studies, Business Administration and Management. Högskolan i Gävle.
    Rudholm, Niklas
    HUI Research.
    Do audited firms have lower cost of debt?2018Report (Other academic)
    Abstract [en]

    The purpose of this study is to investigate if audited financial statements add value for firms in the private debt market. Using an instrumental variable method, we find that firms with audited financial statements, on average, save 1.26 percentage points on cost of debt compared to firms with unaudited financial statements. We also find that using the big, well-known auditing firms does not yield additional cost of debt benefits. Lastly, we find that the effect of audit on cost of debt varies between industries. As such, we find that firms in industries that have been identified in previous studies to have a more complex information structure, and therefore more complex auditing process, also save more on cost of debt relative to other industries when audited.

  • 53.
    Håkansson, Johan
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Isacsson, G.
    The spatial extent of agglomeration economies across the wage earnings distribution2019In: Journal of regional science, ISSN 0022-4146, E-ISSN 1467-9787, Vol. 59, no 2, p. 281-301Article in journal (Refereed)
    Abstract [en]

    We investigate the spatial extent of agglomeration economies across the wage earnings distribution using economic mass (total employment) in four distance bands around each individual’s establishment in a quantile regression framework. We control for observable and unobservable individual and establishment characteristics. Remaining endogeneity in the model is assessed with a set of instrumental variables. Results indicate a positive effect of economic mass on wage earnings up to 25 km away from the establishment. The spatial extent of agglomeration economies is similar across the wage earnings distribution. However, increases in economic mass shift the wage earnings distribution in a nonsymmetric way. 

  • 54.
    Håkansson, Johan
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Lagin, Madelen
    Dalarna University, School of Technology and Business Studies, Business Administration and Management.
    Wennström, Johanna
    Town centre cooperation: Changing perception of property owners2017In: International Journal of Retail & Distribution Management, ISSN 0959-0552, E-ISSN 1758-6690, Vol. 45, no 11, p. 1200-1212Article in journal (Refereed)
    Abstract [en]

    Purpose – The purpose of this paper is to investigate if, and how, different stakeholders perceive property owners (PO) have changed their activities in a town centre after increased competition, and if this has led to a different perception of the PO' stakeholder group. Design/methodology/approach – A comparative follow-up case study is conducted through semi-structured interviews on changes in the town centre management (TCM) stakeholders' perceptions of the role, benefit, and contribution of PO. The interviews are carried out before and after the establishment of a big-box retailer, which makes it possible to analyse possible changes in the perceptions in relation to the overall role of the PO when retail competition increases. Findings – A limited number of PO and local authorities have started working more strategically and proactively by creating a time-restricted alliance that goes beyond the work of the TCM organisation. Although the activities of the PO have increased, this is not fully understood by everyone in the town centre, especially the retailers. Research limitations/implications – In comparison with other studies, this study clearly indicates that the property owner plays a key strategic role in enabling town centre development. This role is broader than what the original TCM literature suggests and is based on the aspects of resource coordination and distribution. Practical implications – In order to create the opportunity to develop a town centre in the long run, it is of strategic importance that the PO are in agreement with the development plans. In addition, it is necessary to consider those members who should be part of the strategic alliance. Originality/value – By conducting a comparative follow-up case study, the authors are able to contribute with a deeper understanding of how stakeholders' perceptions change over time. The authors extend the current literature by showing that the PO are a key stakeholder due to their organisational resources and their ability to facilitate town centre development. © 2017 Emerald Publishing Limited.

  • 55.
    Håkansson, Johan
    et al.
    Dalarna University, School of Technology and Business Studies, Human Geography.
    Li, Yujiao
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Mihaescu, Oana
    HUI Research, Stockholm.
    Rudholm, Niklas
    HUI Research, Stockholm.
    Big-box retail entry in urban and rural areas: Are there productivity spillovers to incumbent retailers?2019In: International Review of Retail Distribution & Consumer Research, ISSN 0959-3969, E-ISSN 1466-4402, Vol. 29, no 1, p. 23-45Article in journal (Refereed)
    Abstract [en]

    This paper empirically measures the potential spillover effects of big-box retail entry on the productivity of incumbent retailers in the entry regions, and investigates whether the effects differ depending on the size of the new establishment relative to the size of the local market. The results indicate that big-box entry increases the productivity of incumbent firms in two of three rural entry regions where the IKEA is large relative to the local retail market, while no productivity spillover effects could be found in the case of the urban IKEA entry in Gothenburg.

    The full text will be freely available from 2020-07-19 08:40
  • 56.
    Iasonidou, Sofia
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Estimating the effect of the 2008 financial crisis on GNI in Greece and Iceland: A synthetic control approach2016Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The purpose of this thesis is to conduct a comparative study in order to estimate the impact of the financial crisis to the GNI of Greece and Iceland. By applying synthetic control matching (a relatively new methodology) the study intends to compare the two countries, thus deducting conclusions about good or bad measures adopted. The results indicate that in both cases the adopted measures were not the optimal ones, since the synthetic counterfactual appear to perform better than the actual Greece and Iceland. Moreover, it is shown that Iceland reacted better to the shock it was exposed. However, different characteristics of the two countries impede the application of Icelandic actions in the Greek case.

  • 57.
    Javed, Farrukh
    et al.
    Business School, Örebro University.
    Thomas, Ilias
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Memedi, Mevludin
    Business School, Örebro University.
    A comparison of feature selection methods when using motion sensors data: a case study in Parkinson’s disease2018In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018, p. 5426-5429Conference paper (Refereed)
    Abstract [en]

    The objective of this study is to investigate the effects of feature selection methods on the performance of machine learning methods for quantifying motor symptoms of Parkinson's disease (PD) patients. Different feature selection methods including step-wise regression, Lasso regression and Principal Component Analysis (PCA) were applied on 88 spatiotemporal features that were extracted from motion sensors during hand rotation tests. The selected features were then used in support vector machines (SVM), decision trees (DT), linear regression, and random forests models to calculate a so-called treatment-response index (TRIS). The validity, testretest reliability and sensitivity to treatment were assessed for each combination (feature selection method plus machine learning method). There were improvements in correlation coefficients and root mean squared error (RMSE) for all the machine learning methods, except DTs, when using the selected features from step-wise regression inputs. Using step-wise regression and SVM was found to have better sensitivity to treatment and higher correlation to clinical ratings on the Unified PD Rating Scale as compared to the combination of PCA and SVM. When assessing the ability of the machine learning methods to discriminate between tests performed by PD patients and healthy controls the results were mixed. These results suggest that the choice of feature selection methods is crucial when working with data-driven modelling. Based on our findings the step-wise regression can be considered as the method with the best performance.

  • 58.
    Jia, Siqi
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    How fair is the so called fair method for resetting the targetin the interrupted men’s one-day international cricket matches?2015Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Duckworth-Lewis (DL) method is used in the One Day International (ODI) cricketmatches when the matches are interrupted. However, all information we have about thismethod is the Duckworth-Lewis calculator, which leads us to suspect the fairness of it.This thesis quantified the effect of the DL method on the result of the matches, whichmeans if teams have the same winning chance under DL method or not. The effect oflikely influential factors, including the use of DL method, on the winning odds areestimated by using the Generalized Linear Mixed Model. The results indicates that theDL method does not have any significant effect on the winning odds, nor does the DLmethod change the effects of other factors. The results also confirm that homeadvantage exists and that winning the coin toss does not affect the outcome of match.

  • 59. Johansson, D.
    et al.
    Ericsson, A.
    Johansson, A.
    Medvedev, A.
    Nyholm, D.
    Ohlsson, F.
    Senek, M.
    Spira, J.
    Thomas, Ilias
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Individualization of levodopa treatment using a microtablet dispenser and ambulatory accelerometry2018In: CNS Neuroscience & Therapeutics, ISSN 1755-5930, E-ISSN 1755-5949, Vol. 24, no 5, p. 439-447Article in journal (Refereed)
    Abstract [en]

    Aim

    This 4‐week open‐label observational study describes the effect of introducing a microtablet dose dispenser and adjusting doses based on objective free‐living motor symptom monitoring in individuals with Parkinson's disease (PD).

    Methods

    Twenty‐eight outpatients with PD on stable levodopa treatment with dose intervals of ≤4 hour had their daytime doses of levodopa replaced with levodopa/carbidopa microtablets, 5/1.25 mg (LC‐5) delivered from a dose dispenser device with programmable reminders. After 2 weeks, doses were adjusted based on ambulatory accelerometry and clinical monitoring.

    Results

    Twenty‐four participants completed the study per protocol. The daily levodopa dose was increased by 15% (112 mg, < 0.001) from period 1 to 2, and the dose interval was reduced by 12% (22 minutes, P = 0.003). The treatment adherence to LC‐5 was high in both periods. The MDS‐UPDRS parts II and III, disease‐specific quality of life (PDQ‐8), wearing‐off symptoms (WOQ‐19), and nonmotor symptoms (NMS Quest) improved after dose titration, but the generic quality‐of‐life measure EQ‐5D‐5L did not. Blinded expert evaluation of accelerometry results demonstrated improvement in 60% of subjects and worsening in 25%.

    Conclusions

    The introduction of a levodopa microtablet dispenser and accelerometry aided dose adjustments improve PD symptoms and quality of life in the short term.

  • 60. Johansson, Dongni
    et al.
    Thomas, Ilias
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Ericsson, Anders
    Johansson, Anders
    Medvedev, Alexander
    Memedi, Mevludin
    Nyholm, Dag
    Ohlsson, Fredrik
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Bergquist, Filip
    Evaluation of a sensor algorithm for motor state rating in Parkinson's disease2019In: Parkinsonism & Related Disorders, ISSN 1353-8020, E-ISSN 1873-5126Article in journal (Refereed)
    Abstract [en]

    INTRODUCTION: A treatment response objective index (TRIS) was previously developed based on sensor data from pronation-supination tests. This study aimed to examine the performance of TRIS for medication effects in a new population sample with Parkinson's disease (PD) and its usefulness for constructing individual dose-response models.

    METHODS: Twenty-five patients with PD performed a series of tasks throughout a levodopa challenge while wearing sensors. TRIS was used to determine motor changes in pronation-supination tests following a single levodopa dose, and was compared to clinical ratings including the Treatment Response Scale (TRS) and six sub-items of the UPDRS part III.

    RESULTS: As expected, correlations between TRIS and clinical ratings were lower in the new population than in the initial study. TRIS was still significantly correlated to TRS (rs = 0.23, P < 0.001) with a root mean square error (RMSE) of 1.33. For the patients (n = 17) with a good levodopa response and clear motor fluctuations, a stronger correlation was found (rs = 0.38, RMSE = 1.29, P < 0.001). The mean TRIS increased significantly when patients went from the practically defined off to their best on state (P = 0.024). Individual dose-response models could be fitted for more participants when TRIS was used for modelling than when TRS ratings were used.

    CONCLUSION: The objective sensor index shows promise for constructing individual dose-response models, but further evaluations and retraining of the TRIS algorithm are desirable to improve its performance and to ensure its clinical effectiveness.

  • 61.
    Jomaa, Diala
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A data driven approach for automating vehicle activated signs2016Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Vehicle activated signs (VAS) display a warning message when drivers exceed a particular threshold. VAS are often installed on local roads to display a warning message depending on the speed of the approaching vehicles. VAS are usually powered by electricity; however, battery and solar powered VAS are also commonplace. This thesis investigated devel-opment of an automatic trigger speed of vehicle activated signs in order to influence driver behaviour, the effect of which has been measured in terms of reduced mean speed and low standard deviation. A comprehen-sive understanding of the effectiveness of the trigger speed of the VAS on driver behaviour was established by systematically collecting data. Specif-ically, data on time of day, speed, length and direction of the vehicle have been collected for the purpose, using Doppler radar installed at the road. A data driven calibration method for the radar used in the experiment has also been developed and evaluated.

    Results indicate that trigger speed of the VAS had variable effect on driv-ers’ speed at different sites and at different times of the day. It is evident that the optimal trigger speed should be set near the 85th percentile speed, to be able to lower the standard deviation. In the case of battery and solar powered VAS, trigger speeds between the 50th and 85th per-centile offered the best compromise between safety and power consump-tion. Results also indicate that different classes of vehicles report differ-ences in mean speed and standard deviation; on a highway, the mean speed of cars differs slightly from the mean speed of trucks, whereas a significant difference was observed between the classes of vehicles on lo-cal roads. A differential trigger speed was therefore investigated for the sake of completion. A data driven approach using Random forest was found to be appropriate in predicting trigger speeds respective to types of vehicles and traffic conditions. The fact that the predicted trigger speed was found to be consistently around the 85th percentile speed justifies the choice of the automatic model.

  • 62.
    Jomaa, Diala
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    The Optimal trigger speed of vehicle activated signs2014Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The thesis aims to elaborate on the optimum trigger speed for Vehicle Activated Signs (VAS) and to study the effectiveness of VAS trigger speed on drivers’ behaviour. Vehicle activated signs (VAS) are speed warning signs that are activated by individual vehicle when the driver exceeds a speed threshold. The threshold, which triggers the VAS, is commonly based on a driver speed, and accordingly, is called a trigger speed. At present, the trigger speed activating the VAS is usually set to a constant value and does not consider the fact that an optimal trigger speed might exist. The optimal trigger speed significantly impacts driver behaviour.

    In order to be able to fulfil the aims of this thesis, systematic vehicle speed data were collected from field experiments that utilized Doppler radar. Further calibration methods for the radar used in the experiment have been developed and evaluated to provide accurate data for the experiment. The calibration method was bidirectional; consisting of data cleaning and data reconstruction. The data cleaning calibration had a superior performance than the calibration based on the reconstructed data.

    To study the effectiveness of trigger speed on driver behaviour, the collected data were analysed by both descriptive and inferential statistics. Both descriptive and inferential statistics showed that the change in trigger speed had an effect on vehicle mean speed and on vehicle standard deviation of the mean speed. When the trigger speed was set near the speed limit, the standard deviation was high. Therefore, the choice of trigger speed cannot be based solely on the speed limit at the proposed VAS location.

    The optimal trigger speeds for VAS were not considered in previous studies. As well, the relationship between the trigger value and its consequences under different conditions were not clearly stated. The finding from this thesis is that the optimal trigger speed should be primarily based on lowering the standard deviation rather than lowering the mean speed of vehicles. Furthermore, the optimal trigger speed should be set near the 85th percentile speed, with the goal of lowering the standard deviation.

  • 63.
    Jomaa, Diala
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Automatic trigger speed for vehicle activated signs using Adaptive Neuro fuzzy system and Random ForestIn: International Journal on Advances in Intelligent Systems, ISSN 1942-2679, E-ISSN 1942-2679Article in journal (Refereed)
  • 64.
    Jomaa, Diala
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Predicting automatic trigger speed for vehicle-activated signs2018In: Journal of Intelligent Systems, ISSN 0334-1860, E-ISSN 2191-026XArticle in journal (Refereed)
    Abstract [en]

    Vehicle-activated signs (VAS) are speed-warning signs activated by radar when the driver speed exceeds a pre-set threshold, i.e. the trigger speed. The trigger speed is often set relative to the speed limit and is displayed for all types of vehicles. It is our opinion that having a static setting for the trigger speed may be inappropriate, given that traffic and road conditions are dynamic in nature. Further, different vehicle classes (mainly cars and trucks) behave differently, so a uniform trigger speed of such signs may be inappropriate to warn different types of vehicles. The current study aims to investigate an automatic VAS, i.e. one that could warn vehicle users with an appropriate trigger speed by taking into account vehicle types and road conditions. We therefore investigated different vehicle classes, their speeds, and the time of day to be able to conclude whether different trigger speeds of VAS are essential or not. The current study is entirely data driven; data are initially presented to a self-organising map (SOM) to be able to partition the data into different clusters, i.e. vehicle classes. Speed, time of day, and length of vehicle were supplied as inputs to the SOM. Further, the 85th percentile speed for the next hour is predicted using appropriate prediction models. Adaptive neuro-fuzzy inference systems and random forest (RF) were chosen for speed prediction; the mean speed, traffic flow, and standard deviation of vehicle speeds were supplied as inputs for the prediction models. The results achieved in this work show that RF is a reliable model in terms of accuracy and efficiency, and can be used in finding appropriate trigger speeds for an automatic VAS. 

  • 65.
    Jomaa, Diala
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A comparative study between vehicle activated signs and speed indicator devices2017In: Transportation Research Procedia, ISSN 2324-9935, E-ISSN 2352-1465, Vol. 22, p. 115-123Article in journal (Refereed)
    Abstract [en]

    Vehicle activated signs and Speed indicator devices are safety signs used to warn and remind drivers that they are exceeding the speed limit on a particular road segment. This article has analysed and compared such signs with the aim of reporting the most suitable sign for relevant situations. Vehicle speeds were recorded at different test sites and the effects of the signs were studied by analyzing the mean and standard deviation. Preliminary results from the work indicate that both types of signs have variable effects on the mean and standard deviation of speed on a given road segment. Speed indicator devices were relatively more effective than vehicle activated signs on local roads; in contrast their effectivity was only comparable when tested on highways.

  • 66.
    Kogo, Gloria
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Analyzing automatic cow recordings to detect the presence of outliers in feed intake data recorded from dairy cows in Lovsta farm2016Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Outliers are a major concern in data quality as it limits the reliability of any data. The

    objective of our investigation was to examine the presence and cause of outliers in the system

    for controlling and recording the feed intake of dairy cows in Lovsta farm, Uppsala Sweden.

    The analyses were made on data recorded as a timestamp of each visit of the cows to

    the feeding troughs from the period of August 2015 to January 2016. A three step

    methodology was applied to this data. The first step was fitting a mixed model to the

    data then the resulting residuals was used in the second step to fit a model based

    clustering for Gaussian mixture distribution which resulted in clusters of which 2.5% of

    the observations were in the outlier cluster. Finally, as the third step, a logistic

    regression was then fit modelling the presence of outliers versus the non-outlier

    clusters. It appeared that on early hours of the morning between 6am to 11.59am, there

    is a high possibility of recorded values to be outliers with odds ratio of 1.1227 and this

    is also the same time frame noted to have the least activity in feed consumption of the

    cows with a decrease of 0.027 kilograms as compared to the other timeframes. These

    findings provide a basis for further investigation to more specifically narrow down the

    causes of the outliers.

  • 67.
    Laryea, Rueben
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A data-driven decision support system for coherency of experts’ judgment in complex classification problems: The case of food security as a UN sustainable development goal2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Everyday humans need to make individual or collective decisions. Often the decisions aim at achieving multiple goals (thus involving multiple criteria) and rely on the decision maker(s)’ intuition, internal data, as well as external sources of data. Faced with a complex decision problem of this kind, it is a great challenge to decisionmakers to be logically coherent over time with regard to their preferences. To aid in achieving coherency, operation researchers and decision analysts have developed formal methods to support decision makers. One such method is the UTADIS method that serves as the workhorse for this thesis. I received the request from UN officials who had to manage the sustainable development goals while addressing the issue of food security. They wished for a decision support system (DSS) that could aid in their classification of countries to mitigate the risk of failing on food security. The virtue of the DSS should be that their expert judgment was complemented by formal methods for better risk classification. The UTADIS method was fitting for the purpose, but it lacked implementability. In particular, it required an iterative approach engaging the experts multiple times, while not readily lending itself to making use of external data, making it inefficient as a DSS. The fundamental contribution of this thesis is that I have solved these shortcomings of the UTADIS method, such that it now readily can be used in a functionally efficient way for the desired purpose of the UN. In solving these problems, it is also more broadly implementable as a DSS, as I have validated the artifact to a DSS, by use of several demonstrations and exposed it to sensitivity analysis.

  • 68.
    Laryea, Rueben
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Carling, Kenneth
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Cialani, Catia
    Dalarna University, School of Technology and Business Studies, Economics.
    A Food Price Volatility Model for Country Risk Classification2018In: International Journal of Risk Assessment and Management, ISSN 1466-8297, E-ISSN 1741-5241Article in journal (Refereed)
    Abstract [en]

    Decision makers require risk models which satisfies their preferences in decision making processes. A methodological approach to presenting a decision model that satisfies the preferences of the decision maker and aids the decision maker to classify countries into crisis groups based on the price volatility of food staple criteria is discussed in this paper. The price volatility of food staples is obtained from time series plots and a Multi-Criteria Decision Analysis method, the UTilitdditives DIScriminantes (UTADIS) classification methodological framework is applied on the price volatility data to develop a food price volatility classification model which suits the decision maker’s preferences. The methodological framework is better applied in this paper by aiding the decision maker to make informed judgements on the price volatility of food staples in predefining their risk classes. This introduces efficiency in the application of the methodological classification framework, by reducing to the barest minimum level, the misclassification errors between the decision makers preferred classification and the UTADIS method’s classification which estimates the utility function or classification model and the utility threshold or cut-off points which would classify the country alternatives into their authentic or original classes with the execution of the methodological framework just once. The resulting utility function or classification model is thus accurate enough to satisfy the preferences of the decision maker in classifying future datasets.

  • 69.
    Laryea, Rueben
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Carling, Kenneth
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Cialani, Catia
    Dalarna University, School of Technology and Business Studies, Economics.
    Nyberg, Roger G.
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Sensitivity analysis of a risk classification model for food price volatility2018In: International Journal of Risk Assessment and Management, ISSN 1466-8297, E-ISSN 1741-5241, Vol. 21, no 4, p. 374-382Article in journal (Refereed)
    Abstract [en]

    A sensitivity analysis to vary the weights of an accurate predictive classification model to produce a mixed model for ranking countries on the risk of food price volatility is carried out in this paper. The classification model is a marginal utility function consisting of multiple criteria. The aim of the sensitivity analysis is to derive a mixed model to be used in ranking of country alternatives to aid in policy formulation. Since in real-life situations the data that goes into decision making could be subjected to possibilities of alterations over time, it is essential to aid decision makers to vary the weights of the criteria using both subjective and objective information to introduce imprecision and to generate relative values of the criteria with a scale to form a mixed model. The mixed model can be used to rank future relative alternative value data sets for policy formulation.

  • 70.
    Laryea, Rueben
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Farsari, Ioanna
    Dalarna University, School of Technology and Business Studies, Tourism Studies.
    Nyberg, Roger G.
    Dalarna University, School of Technology and Business Studies, Information Systems.
    A Decision Tool Approach to Sensitivity Analysis in a Risk Classification Model2018In: Article in journal (Refereed)
    Abstract [en]

    A Decision Analytical tool capable of handling numerically imprecise data for decision making is used in this paper to analyze the risk of the effect of data alteration in the ranking positions of country alternatives for food price volatility. Unguided decision making processes would lead to non-optimal decisions with it’s dire consequences on the resources of organizations. The paper is thus guided by the use of an accurate risk classification model to implement uncertainty and imprecision which are essential part of real life decision making processes with computer based tools to overcome the problem of possibilities uncertain and imprecise input data of criteria and alternatives. A ranking of the alternatives is conducted after imprecision is considered in the input data and a further analysis is carried out to determine which criteria is sensitive enough to alter the position of a country in the rankings.

  • 71.
    Li, Boyan
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A comparison of hurdle method and universal kriging for predicting spatially correlated count response with excessive zeros2015Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    A hurdle model combined with Bernoulli part and truncated Poisson part can be used to predict zero-inflated geographic count response. To get the prediction with a hurdle model, the estimation of fixed effects can be easily solved as generalized linear model (GLM) does. An ad-hoc method, which re-fits the hurdle model to compute the predicted random effect for geographic IDs with missing response, is applied. However, no study has examined the performance of this prediction method for hurdle model, especially for the spatially correlated count responses with excessive zeros. This paper aims to check how well the hurdle predictors perform in ideal and real situations, by means of cross validation. The performance of the hurdle model based prediction is compared with the performance of the predictors from the universal kriging which is most widely used on spatial predictions. The simulation result shows that hurdle performs better than universal kriging based on mean absolute errors. The ideal situation is generated by using Monte-Carlo simulation. In order to examine the comparative performance with real data situations, two real data examples are presented. The results show that, in prediction using single observation per location (e.g. one year’s spatial observation) with excessive zeros, hurdle model does not perform well, while universal kriging also failed in the same situations especially for those non-zero points.

  • 72.
    Li, Yujiao
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Who benefits when IKEA enters local markets in Sweden?: An empirical assessment using difference-in-difference analysis, synthetic control methods, and Twitter sentiment analysis2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Policy makers often spend considerable amounts of money to attract IKEA to their region despite not having any empirical measurements on its expected contribution to the local economy. As such, an empirical study of the economic and social impact of new IKEA stores can aid political decision making, and contribute to the literature regarding how big-box retail entry affects the regions where they enter.

    This dissertation aims to estimate: the impact of IKEA entry on incumbent retailers productivity, and investigate if the impact is heterogenus depending on local maket size, type of retail industry, distance to surrounding retailers, and firm size; IKEA entry effects on the average labor productivity in durable goods retailing in the entry regions; and, finally, public opinions regarding  IKEA entry.

    For IKEA entry effects on incumbent retailers, Paper I~III separately examine four factors of potential heterogeneity. Paper I finds that market size matters: smaller rural regions have bigger IKEA effects. Paper II considers two factors: firm industry and distance, and confirms that IKEA entry effects dissipate over distance. The positive impact of IKEA entry on incumbent retailers is limited to those selling complementary goods to IKEA. No positive effects were found for the urban entry in Gothenburg in the two first papers, which is somewhat surprising. Paper III found that a positive effect exist also in Gothenburg, but it is limited to relatively small incumbent retailers with a capital stock below 1 500 000 SEK. Policy making tends to consider IKEA overall effects on entry municipalities besides IKEA spillover effects on firms. Paper V shows that rural regions are affected by IKEA entry, while larger urban markets are not.

    For the social effects of IKEA, Paper VI uses Twitter text mining to study public opinions regarding IKEA entry into local markets. The new IKEA stores under study caught significant public attention at the time of entry, with mostly positive attitudes toward the new stores. The favorite topics for discussion at the time of the different IKEA entries were heterogeneous depending on location.

    Methodologically, Paper I uses traditional Difference-in-Difference (DID) to have an initial understanding of IKEA entry spillover effects in four regions; Paper II extends to Spatial DID to catch the spatial interaction between firms; Paper III uses Panel Smooth Transition Regression to identify heterogenous effects due to firms size. Paper IV investigates a new treatment effects estimation aproach, Synthetic Control Method (SCM), to explore when the SCM is powerful, and how to improve its performance; Paper V then uses SCM to estimate IKEA effects at municipality level. In addition, to make SCM developed readily available for other researchers, the author of this thesis also published one web-application to implement a synthetic control method power test, and another to implement parametric & non-parametric estimation and inference.  

    These findings confirm that IKEA has a positive effect on the regions where they enter. Nevertheless, governments that are to decide if to allow a big-box retail entry into their local community should be aware that the impact of such entry will depend on the size of the existing retail market, the type of existing retail industry, and the size of existing retailers in the entry region.

  • 73.
    Li, Yujiao
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Twitter Sentiment Analysis of New IKEA Stores Using Machine Learning2018In: 2018 International Conference on Computer and Applications, ICCA 2018, 2018, p. 4-11, article id 8460277Conference paper (Refereed)
    Abstract [en]

    This paper studied public emotion and opinion concerning the opening of new IKEA stores, specifically, how much attention are attracted, how much positive and negative emotion are aroused, what IKEA-related topics are talked due to this event. Emotion is difficult to measure in retail due to data availability and limited quantitative tools. Twitter texts, written by the public to express their opinion concerning this event, are used as a suitable data source to implement sentiment analysis. Around IKEA opening days, local people post IKEA related tweets to express their emotion and opinions on that. Such “IKEA” contained tweets are collected for opinion mining in this work. To compute sentiment polarity of tweets, lexiconbased approach is used for English tweets, and machine learning methods for Swedish tweets. The conclusion is new IKEA store are paid much attention indicated by significant increasing tweets frequency, most of them are positive emotions, and four studied cities have different topics and interests related IKEA. This paper extends knowledge of consumption emotion studies of prepurchase, provide empirical analysis of IKEA entry effect on emotion. Moreover, it develops a Swedish sentiment prediction model, elastic net method, to compute Swedish tweets’ sentiment polarity which has been rarely conducted.  

  • 74.
    Li, Yujiao
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Human Geography.
    Mihaescu, Oana
    HUI Research, Stockholm, Sweden.
    Rudholm, Niklas
    HUI Research, Stockholm, Sweden.
    Agglomeration economies in urban retailing: Are there productivity spillovers when big-box retailers enter urban markets?2019In: Applied Economics Letters, ISSN 1350-4851, E-ISSN 1466-4291Article in journal (Other academic)
    Abstract [en]

    Previous studies have found that big-box retail entry does not affect the productivity of incumbent retailers when entry occurs in urban areas. In this paper, we show that there are positive spillover effects of big-box retail entry to incumbent retailers in urban areas as well, but that these are limited to relatively small retailers, making the effects difficult to detect using traditional econometric methods, such as difference-in-difference estimation on the full sample of firms. In a two-step procedure, we first use panel smooth transition regression to determine size thresholds that delimit incumbent retail firms by their possible reactions to the new big-box entry. We then use difference-in-difference estimations on these subgroups of firms to determine, within each group, the direction and magnitude of the effects of big-box entry on their productivity. For the group of small incumbent retailers, we find positive spillover effects on productivity of approximately 9%.

  • 75.
    Lin, Chenlu
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A Combined Approach to Recommendation Systems: A case study of data analysis for hotel ratings2015Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Recommendation systems are used to improve the convenience and efficiency for users tobook hotels. The most widely used method in recommendation systems is collaborativefiltering. A critical step of the collaborative filtering method is to analyze one user'spreference and recommend products or services to the user based on other similar users'preferences. However, collaborative filtering is vulnerable for recommendation when it isdifficult to obtain user preferences, in the situation where e.g. a user provides none or veryfew comments on products or services. The problem occurring in this situation is called thecold start problem. This thesis proposes an improved method which combines collaborativefiltering with data classification to recommend suitable hotels to new users. The accuracy ofthe recommendation is determined by the rankings so that evaluations are conducted on theTop-3 and the Top-10 recommendation lists using the 10-fold cross-validation method andROC curves. The results show that the Top-3 hotel recommendation list proposed by thecombined method has the superiority of the recommendation performance than the Top-10 listunder the cold start condition in most of the times.

  • 76.
    Lindgren, Charlie
    et al.
    Dalarna University, School of Technology and Business Studies, Economics.
    Huq, Asif
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Li, Yujiao
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Elbe, Jörgen
    Dalarna University, School of Technology and Business Studies, Business Administration and Management.
    Carling, Kenneth
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Current practices of CSR around the globe: An exploratory text mining study2019Conference paper (Refereed)
  • 77.
    Luo, Xin
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    The causal effect of fertility on Swedish mothers’ labor supply2015Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The objective of this thesis is to estimate causal effect of childbearing on women’slabor supply in Sweden. I follow the approach suggested by Angrist and Evans (1998)using parental preferences for a mixed child-gender composition as an exogenoussource of variation in women’s fertility. The results show that having an additionchild have a negative effect on women’s working hours. However, none of theseeffects are statistical significant and the value of F-statistic and partial-R2 are rathersmall, all suggest that the same-sex is very likely a weak instrument in Sweden.

  • 78.
    Macuchova, Zuzana
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Entry, re-location and growth in the Swedish wholesale trade industry2013Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Wholesale trade has an intermediate position between manufacturing and retail in the distributional channel. In modern economies, consumers buy few, if any, products directly from manufacture or producer. Instead, it is a wholesaler, who is in direct contact with producers, buying goods in larger quantities and selling them in smaller quantities to retailers. Traditionally, the main function of a wholesaler has been to push goods along the distributional channel from producer to retailer, or other nonend user. However, the function of wholesalers usually goes beyond the process of the physical distribution of goods. Wholesalers also arrange storage, perform market analyses, promote trade or provide technical support to consumers (Riemers 1998). The existence of wholesalers (and other intermediaries) in the distributional channel is based on the effective and efficient performance of distribution services, that are needed by producers and other members of the supply chain. Producers usually do not enjoy the economies of scale that they have in production, when it comes to providing distributional services (Rosenbloom 2007) and this creates a space for wholesalers or other intermediaries. Even though recent developments in the distributional channel indicate that traditional wholesaling activities now also compete with other supply chain organizations, wholesaling still remains an important activity in many economies (Quinn and Sparks, 2007).

    In 2010, the Swedish wholesale trade sector consisted of approximately 46.000 firms and generated an annual turnover of 1 300 billion SEK (Företagsstatistiken, Statistics Sweden). In terms of turnover, wholesaling accounts for 20% of the gross domestic product and is thereby the third largest industry. This is behind manufacturing and a composite group of firms in other sectors of the service industry but ahead of retailing. This indicates that the wholesale trade sector is an important part of the Swedish economy. The position of wholesaling is further reinforced when measuring productivity growth. Measured in terms of value added per employee, wholesaling experienced the largest productivity growth of all industries in the Swedish economy during the years 2000 through 2010.

    The fact that wholesale trade is one of the important parts of a modern economy, and the positive development of the Swedish wholesale trade sector in recent decades, leads to several questions related to industry dynamics. The three topics that will be examined in this thesis are firm entry, firm relocation and firm growth. The main question to be answered by this thesis is what factors influence new firm formation, firm relocation and firm growth in the Swedish wholesale trade sector?

  • 79.
    Macuchova, Zuzana
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Essays on firm dynamics in the Swedish wholesale trade sector2015Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis consists of a summary and five self-contained papers addressing dynamics of firms in the Swedish wholesale trade sector.

    Paper [1] focuses upon determinants of new firm formation in the Swedish wholesale trade sector, using two definitions of firms’ relevant markets, markets defined as administrative areas, and markets based on a cost minimizing behavior of retailers. The paper shows that new entering firms tend to avoid regions with already high concentration of other firms in the same branch of wholesaling, while right-of-the-center local government and quality of the infrastructure have positive impacts upon entry of new firms. The signs of the estimated coefficients remain the same regardless which definition of relevant market is used, while the size of the coefficients is generally higher once relevant markets delineated on the cost-minimizing assumption of retailers are used.

    Paper [2] analyses determinant of firm relocation, distinguishing between the role of the factors in in-migration municipalities and out-migration municipalities. The results of the analysis indicate that firm-specific factors, such as profits, age and size of the firm are negatively related to the firm’s decision to relocate. Furthermore, firms seems to be avoiding municipalities with already high concentration of firms operating in the same industrial branch of wholesaling and also to be more reluctant to leave municipalities governed by right-of-the- center parties. Lastly, firms seem to avoid moving to municipalities characterized with high population density.

    Paper [3] addresses determinants of firm growth, adopting OLS and a quantile regression technique. The results of this paper indicate that very little of the firm growth can be explained by the firm-, industry- and region-specific factors, controlled for in the estimated models. Instead, the firm growth seems to be driven by internal characteristics of firms, factors difficult to capture in conventional statistics. This result supports Penrose’s (1959) suggestion that internal resources such as firm culture, brand loyalty, entrepreneurial skills, and so on, are important determinants of firm growth rates.

    Paper [4] formulates a forecasting model for firm entry into local markets and tests this model using data from the Swedish wholesale industry. The empirical analysis is based on directly estimating the profit function of wholesale firms and identification of low- and high-return local markets. The results indicate that 19 of 30 estimated models have more net entry in high-return municipalities, but the estimated parameters is only statistically significant at conventional level in one of our estimated models, and then with unexpected negative sign.

    Paper [5] studies effects of firm relocation on firm profits of relocating firms, employing a difference-in-difference propensity score matching. Using propensity score matching, the pre-relocalization differences between relocating and non-relocating firms are balanced, while the difference-in-difference estimator controls for all time-invariant unobserved heterogeneity among firms. The results suggest that firms that relocate increase their profits significantly, in comparison to what the profits would be had the firms not relocated. This effect is estimated to vary between 3 to 11 percentage points, depending on the length of the analyzed period. 

  • 80.
    Macuchova, Zuzana
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Brandt, Daniel
    Dalarna University, School of Technology and Business Studies, Human Geography.
    Vinterturismens utveckling 2012-2017: En kartläggning av gästnätternas fördelning och utveckling på kommunnivå i Dalarnas län2017Report (Other academic)
  • 81.
    Mahbub, Cynthia
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    The match of demand and supply of public transportation (bus) services in Borlänge, Dalarna.2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Accessibility to public transport service allows mobility of people who do not have access to private cars and at the same time reduces adverse effects of motorized vehicles such as energy consumption, air pollution, etc. Government body promotes to use public transport to facilitate better living condition. However, a critical issue remains whether the public transportation services are sufficient to meet the demanded public transportation services.

    In this research, particular attention has been paid to the spatial transport service gap assessment by analyzing the demand and the supply of the public transportation services in Borlänge. The spatial aspects have been chosen based on Swedish socio-economic condition. The aim of the research is to find a generic methodology to ascertain the disparity between public transport demand and available supply of public transport especially on bus line 211, 213 & 216 in Borlänge Municipality and to visualize the disparity of transportation service using Geographical Information System (GIS) application at different areas along the bus line.

    The result indicates that existing public transport provided by Dalatrafik has a significant gap in Tronsjö, Milsbosjön, Milsbo and Viksnäs between delivered transport supply and possible transportation service needed. This transportation gap may occur due to the deficiency of service capacity and low frequency of the services. Moreover, some topics can be explored for further research such as temporal service gap analysis at each area, find alternative means of transport, flexible transportation service and etc. to improve the public transportation system in Borlänge.

  • 82.
    Malek, Wasim
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Big Data Analysis in Social Networks: Extracting Food Preferences of Vegans from Twitter2016Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Market research is often conducted through conventional methods such as surveys, focus

    groups and interviews. But the drawbacks of these methods are that they can be costly and timeconsuming.

    This study develops a new method, based on a combination of standard techniques

    like sentiment analysis and normalisation, to conduct market research in a manner that is free

    and quick. The method can be used in many application-areas, but this study focuses mainly on

    the veganism market to identify vegan food preferences in the form of a profile.

    Several food words are identified, along with their distribution between positive and negative

    sentiments in the profile. Surprisingly, non-vegan foods such as cheese, cake, milk, pizza and

    chicken dominate the profile, indicating that there is a significant market for vegan-suitable

    alternatives for such foods. Meanwhile, vegan-suitable foods such as coconut, potato,

    blueberries, kale and tofu also make strong appearances in the profile.

    Validation is performed by using the method on Volkswagen vehicle data to identify positive

    and negative sentiment across five car models. Some results were found to be consistent with

    sales figures and expert reviews, while others were inconsistent. The reliability of the method

    is therefore questionable, so the results should be used with caution.

  • 83.
    Martín-Roldán Villanueva, Gonzalo
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Household’s energy consumption and productionforecasting: A Multi-step ahead forecast strategiescomparison.2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In a changing global energy market where the decarbonization of the economy and

    the demand growth are pushing to look for new models away from the existing

    centralized non-renewable based grid. To do so, households have to take a

    ‘prosumer’ role; to help them take optimal actions is needed a multi-step ahead

    forecast of their expected energy production and consumption. In multi-step ahead

    forecasting there are different strategies to perform the forecast. The single-output:

    Recursive, Direct, DirRec, and the multi-output: MIMO and DIRMO. This thesis

    performs a comparison between the performance of the differents strategies in a

    ‘prosumer’ household; using Artificial Neural Networks, Random Forest and

    K-Nearest Neighbours Regression to forecast both solar energy production and

    grid input. The results of this thesis indicates that the methodology proposed

    performs better than state of the art models in a more detailed household energy

    consumption dataset. They also indicate that the strategy and model of choice is

    problem dependent and a strategy selection step should be added to the forecasting

    methodology. Additionally, the performance of the Recursive strategy is always

    far from the best while the DIRMO strategy performs similarly. This makes the

    latter a suitable option for exploratory analysis.

  • 84. Matic, T
    et al.
    Aghanavesi, Somayeh
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Memedi, M.
    Nyholm, D.
    Bergquist, F.
    Groznik, V.
    Zabkar, J.
    Sadikov, A.
    Unsupervised learning from motion sensor data to assess the condition of patients with parkinson's disease2019Conference paper (Refereed)
    Abstract [en]

    Parkinson’s disease (PD) is a chronic neurodegenerative disorder that predominantly affects the patient’s motor system, resulting in muscle rigidity, bradykinesia, tremor, and postural instability. As the disease slowly progresses, the symptoms worsen, and regular monitoring is required to adjust the treatment accordingly. The objective evaluation of the patient’s condition is sometimes rather difficult and automated systems based on various sensors could be helpful to the physicians. The data in this paper come from a clinical study of 19 advanced PD patients with motor fluctuations. The measurements used come from the motion sensors the patients wore during the study. The paper presents an unsupervised learning approach applied on this data with the aim of checking whether sensor data alone can indicate the patient’s motor state. The rationale for the unsupervised approach is that there was significant inter-physician disagreement on the patient’s condition (target value for supervised machine learning). The input to clustering came from sensor data alone. The resulting clusters were matched against the physicians’ estimates showing relatively good agreement. 

  • 85.
    May, Ross
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    An Empirical Investigation of the Merits of a Classof Analytically Tractable Matern CovarianceStructures in Spatial Data Analysis2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    I investigate, using the R package

    spaMM, the effect of misspecification of the

    smoothing parameter,

    􀁑, of the Matern covariance structure on the mean part of

    hierarchical generalised linear models (HGLMs) with spatially correlated Gaussian

    Matern random effects. In particular, by restricting

    􀁑 to the set {0.5, 1.5, 2.5} I

    examine via a simulation study the amount of bias introduced on the fixed effects

    estimates in which the data used to fit the model was generated with different

    values to the aforementioned set. The effect of misspecification was found to be

    minimal.

    By restricting the smoothing parameter,

    􀁑, to the set {0.5, 1.5, 2.5} I utilise the R

    package

    hglm, to develop a procedure (MaternHGLM) for fitting spatial Matern

    HGLMs. In particular, I constructed a hierarchical likelihood (h-likelihood)

    function with given correlation parameters which thus enabled me to Choleski

    decompose the Matern covariance matrix and utilise

    hglm to estimate fixed and

    random effects along with dispersion parameters. Using the above estimated

    parameters I then formed an adjusted profile h-likelihood for the estimation of the

    Matern scaling parameter,

    􀁕, using the Newton-Raphson procedure. Simulation

    studies were carried out to assess the computational efficiency of

    MaternHGLM

    compared to

    spaMM. I found that, on average, MaternHGLM was 136% faster

    than

    spaMM.

    I also analysed two real world datasets using both

    spaMM and MaternHGLM.

    By fixing

    􀁑 at the most appropriate value from the set {0.5, 1.5, 2.5} I examined to

    what extent, if any, did the conclusions drawn differ from those in the original

    study. I found that in general the conclusions were the same, however, on one of

    the datasets

    spaMM’s conclusion didn’t align at all with the original analysis even

    with

    􀁑 estimated from the data.

  • 86.
    Meng, Xiangli
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Optimization heuristic solutions, how good can they be?: With empirical applications in location problems2015Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Combinatorial optimization problems, are one of the most important types of problems in operational research. Heuristic and metaheuristics algorithms are widely applied to find a good solution. However, a common problem is that these algorithms do not guarantee that the solution will coincide with the optimum and, hence, many solutions to real world OR-problems are afflicted with an uncertainty about the quality of the solution. The main aim of this thesis is to investigate the usability of statistical bounds to evaluate the quality of heuristic solutions applied to large combinatorial problems. The contributions of this thesis are both methodological and empirical. From a methodological point of view, the usefulness of statistical bounds on p-median problems is thoroughly investigated. The statistical bounds have good performance in providing informative quality assessment under appropriate parameter settings. Also, they outperform the commonly used Lagrangian bounds. It is demonstrated that the statistical bounds are shown to be comparable with the deterministic bounds in quadratic assignment problems. As to empirical research, environment pollution has become a worldwide problem, and transportation can cause a great amount of pollution. A new method for calculating and comparing the CO2-emissions of online and brick-and-mortar retailing is proposed. It leads to the conclusion that online retailing has significantly lesser CO2-emissions. Another problem is that the Swedish regional division is under revision and the border effect to public service accessibility is concerned of both residents and politicians. After analysis, it is shown that borders hinder the optimal location of public services and consequently the highest achievable economic and social utility may not be attained.

  • 87. Meng, Xiangli
    et al.
    Carling, Kenneth
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Rebreyend, Pascal
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    How do administrative borders affect accessibility to hospitals? The case of Sweden2018In: International Journal of Health Planning and Management, ISSN 0749-6753, E-ISSN 1099-1751, Vol. 33, no 3Article in journal (Refereed)
    Abstract [en]

    An administrative border might hinder the optimal allocation of a given set of resources by restricting the flow of goods, services, and people. In this paper, we address the question: Do administrative borders lead to poor accessibility to public service? In answering the question, we have examined the case of Sweden and its regional administrative borders and hospital accessibility. We have used detailed data on the Swedish road network, its hospitals, and its geo-coded population. We have assessed the population's spatial accessibility to Swedish hospitals by computing the inhabitants' distance to the nearest hospital. We have also elaborated several scenarios ranging from strongly confining regional borders to no confinements of borders and recomputed the accessibility. Our findings imply that administrative borders are only marginally worsening the accessibility.

  • 88.
    Nordström, Carin
    et al.
    Dalarna University, School of Technology and Business Studies, Business Administration and Management.
    Farsari, Ioanna
    Dalarna University, School of Technology and Business Studies, Tourism Studies.
    Zhao, Xiaoyun
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Ramsay, Sarah
    Working with Swedes2018Book (Other academic)
  • 89.
    Paidi, Vijay
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Developing decision support systems for last mile transportation problems2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Last mile transportation is the most problematic phase of transportation needing additional research and effort. Longer waits or search times, lack of navigational directions and real-time information are some of the common problems associated with last mile transportation. Inefficient last mile transportation has an impact on the environment, fuel consumption, user satisfaction and business opportunities. Last mile problems exist in several transportation domains, such as: the landing of airplanes, docking of ships, parking of vehicles, attended home deliveries, etc. While there are dedicated inter-connected decision support systems available for ships and aircraft, similar systems are not widely utilized in parking or attended handover domains. Therefore, the scope of this thesis covers last mile transportation problems in parking and attended handover domains. One problem area for parking and attended handovers is due to lack of real-time information to the driver or consumer. The second problem area is dynamic scheduling where the handover vehicle must traverse additional distance to multiple handover locations due to lack of optimized routes. Similarly, during parking, lack of navigational directions to an empty parking space can lead to increased fuel consumption and CO2 emissions. Therefore, aim of this thesis is to design and develop decision support systems for last mile transportation problems by holistically addressing real time customer communication and dynamic scheduling problem areas. The problem areas discussed in this thesis consists of persistent issues even though they were widely discussed in the literature. In order to investigate the problem areas, microdata analysis approach was implemented in the thesis. The phases involved in Microdata analysis are: data collection, data processing, data storage, data analysis and decision-making. Other similar research domains, such as: computer science or statistics also involve phases such as data collection, processing, storage and analysis. These research domains also work in the fields of decision support systems or knowledge creation. However, knowledge creation or decision support systems is not a mandatory phase in these research domains, unlike Microdata analysis. Three papers are presented in this thesis, with two papers focusing on parking domains, while the third paper focuses on attended handover domains.

    The first paper identifies available smart parking tools, applications and discusses their uses and drawbacks in relation to open parking lots. The usage of cameras in identifying parking occupancy was recognized as one of the suitable tools in this paper. The second paper uses a thermal camera to collect the parking lot data, while deep learning methodologies were used to identify parking occupancy detection. Multiple deep learning networks were evaluated for identifying parking spaces and one method was considered suitable for acquiring real time parking occupancy. The acquired parking occupancy information can be communicated to the user to address real-time customer communication problems. However, the decision support system (DSS) to communicate parking occupancy information still needs to be developed. The third paper focuses on the attended handovers domain where a decision support system was reported which addresses real-time customer communication and dynamic scheduling problems holistically. Based on a survey, customers accepted the use of mobile devices for enabling a real-time information flow for improving customer satisfaction. A pilot test on vehicle routing was performed where the decision support system reduced the vehicle routing distance compared to the route taken by the driver. The three papers work in developing decision support systems for addressing major last mile transportation problems in parking and attended handover domains, thus improving customer satisfaction, and business opportunities, and reducing fuel costs, and pollution.

  • 90.
    Paidi, Vijay
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Parking Occupancy Detection Using Thermal Camera2019In: Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, 2019, p. 483-490Conference paper (Refereed)
    Abstract [en]

    Parking a vehicle is a daunting task during peak hours. The search for a parking space leads to congestion and increased air pollution. Information of a vacant parking space would facilitate to reduce congestion and subsequent air pollution. This paper aims to identify parking occupancy in an open parking lot which consists of free parking spaces using a thermal camera. A thermal camera is capable of detecting vehicles in any weather and light conditions based on emitted heat and it can also be installed in public places with less restrictions. However, a thermal camera is expensive compared to a colour camera. A thermal camera can detect vehicles based on the emitted heat without any illumination. Vehicles appear bright or dark based on heat emitted by the vehicles. In order to identify vehicles, pre-trained vehicle detection algorithms, Histogram of Oriented Gradient detectors, Faster Regional Convolutional Neural Network (FRCNN) and modified Faster RCNN deep learning networks were implemented in this paper. The detection rates of the detectors reduced with diminishing of heat in the vehicles. Modified Faster RCNN deep learning network produced better detection results compared to other detectors. However, the detection rates can further be improved with larger and diverse training dataset.

  • 91.
    Paidi, Vijay
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Nyberg, Roger G.
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Smart parking sensors, technologies and applications for open parking lots: a review2018In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 12, no 8, p. 735-741Article in journal (Refereed)
    Abstract [en]

    Parking a vehicle in traffic dense environments often leads to excess time of driving in search for free space which leads to congestions and environmental pollution. Lack of guidance information to vacant parking spaces is one reason for inefficient parking behaviour. Smart parking sensors and technologies facilitate guidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensors or technologies is in use for open parking lot. This paper reviews the literature on the usage of smart parking sensors, technologies, applications and evaluate their applicability to open parking lots. Magnetometers, ultrasonic sensors and machine vision were few of the widely used sensors and technologies on closed parking lots. However, this paper suggests a combination of machine vision, convolutional neural network or multi-agent systems suitable for open parking lots due to less expenditure and resistance to varied environmental conditions. Few smart parking applications show drivers the location of common open parking lots. No application provided real time parking occupancy information, which is a necessity to guide them along the shortest route to free space. To develop smart parking applications for open parking lots, further research is needed in the fields of deep learning and multi-agent systems.

  • 92.
    Paidi, Vijay
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Nyberg, Roger G.
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Smart Parking Tools Suitability for Open Parking Lots: A Review2018In: Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems, Madeira, 2018, p. 600-609Conference paper (Refereed)
    Abstract [en]

    Parking a vehicle in traffic dense environments is a common issue in many parts of the world which oftenleads to congestion and environmental pollution. Lack of guidance information to vacant parking spaces isone of the reasons for inefficient parking behaviour. Smart parking sensors and technologies facilitateguidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensorsor technologies are in use for the common open parking lot. This paper reviews the literature on the usage ofsmart parking sensors, technologies, applications and evaluate their suitability to open parking lots. Suitabilitywas made in terms of expenditure and reliability. Magnetometers, ultrasonic sensors and machine vision werefew of the widely used sensors and technologies used in closed parking lots. However, this paper suggests acombination of machine vision, fuzzy logic or multi-agent systems suitable for open parking lots due to lessexpenditure and resistance to varied environmental conditions. No application provided real time parkingoccupancy information of open parking lots, which is a necessity to guide them along the shortest route tofree space. To develop smart parking applications for open parking lots, further research is needed in the fieldsof deep learning.

  • 93.
    Paidi, Vijay
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Nyberg, Roger G.
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Deep learning-based vehicle occupancy detection in an open parking lot using thermal camera2019In: Article in journal (Other academic)
    Abstract [en]

    Parking vehicle is a daunting task and a common problem in many cities around the globe. The search for parking space leads to congestion, frustration and increased air pollution. Information of a vacant parking space would facilitate to reduce congestion and subsequent air pollution. Therefore, aim of the paper is to acquire vehicle occupancy in an open parking lot using deep learning. Thermal camera was used to collect the data during varying environmental conditions such as; sunny, dusk, dawn, dark and snowy conditions. Vehicle detection with deep learning was implemented where image classification and object localization were performed for multi object detection. The dataset consists of 527 images which were manually labelled as there were no pre-labelled thermal images available. Multiple deep learning networks such as Yolo, ReNet18, ResNet50 and GoogleNet with varying layers and architectures were evaluated on vehicle detection. Yolo, GoogleNet and ResNet18 are computationally efficient detectors which took less processing time while Resnet50 produced better detection results compared to other detectors. However, ResNet18 also produced minimal miss rates and is suitable for real time vehicle detection. The detected results were compared with a template of parking spaces and IoU value is used to identify vehicle occupancy information.

    The full text will be freely available from 2019-10-31 21:26
  • 94.
    Paidi, Vijay
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Nyberg, Roger G.
    Dalarna University, School of Technology and Business Studies, Information Systems.
    A holistic decision support system for last mile handovers2019In: Article in journal (Other academic)
    Abstract [en]

    The last mile handover is assumed to be the most problematic part in the delivery process and the costs can go upto 50% of the total logistic cost. Real time consumer communication and dynamic scheduling are the major problem areas associated with effective attended last mile handovers. Therefore, aim of this paper is to report the design and development of a holistic decision support system’s functionalities which simultaneously addresses real time consumer communication and dynamic scheduling. A decision support system was designed and developed based on workshops, expert group interviews and its functionalities were proposed with the use cases. A survey was conducted with consumers of a retailer where majority of the consumers accepted the use of mobile communication devices to enable real time communication and alternate handover location which improves customer satisfaction and facilitates to avoid missed handovers. A pilot test was performed where routing distance was reduced with the use of optimized handover routes. However the improvement is subjected to the experience of driver and real time traffic conditions. We conclude that a holistic decision support system with multi-party communication among the stakeholders facilitates in reducing operational costs for logistic companies and improving customer satisfaction and business opportunities.

    The full text will be freely available from 2019-11-30 21:30
  • 95. Pan, S
    et al.
    Xiong, Y
    Han, Y
    Zhang, Xingxing
    Dalarna University, School of Technology and Business Studies, Energy Technology.
    Xia, L
    Wei, S
    Wu, J
    Han, Mengjie
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A study on influential factors of occupant window-opening behavior in an office building in China2018In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 133, p. 41-50Article in journal (Refereed)
    Abstract [en]

    Occupants often perform many types of behavior in buildings to adjust the indoor thermal environment. In these types, opening/closing the windows, often regarded as window-opening behavior, is more commonly observed because of its convenience. It not only improves indoor air quality to satisfy occupants' requirement for indoor thermal comfort but also influences building energy consumption. To learn more about potential factors having effects on occupants' window-opening behavior, a field study was carried out in an office building within a university in Beijing. Window state (open/closed) for a total of 5 windows in 5 offices on the second floor in 285 days (9.5 months) were recorded daily. Potential factors, categorized as environmental and non-environmental ones, were subsequently identified with their impact on window-opening behavior through logistic regression and Pearson correlation approaches. The analytical results show that occupants' window-opening behavior is more strongly correlated to environmental factors, such as indoor and outdoor air temperatures, wind speed, relative humidity, outdoor FM2.5 concentrations, solar radiation, sunshine hours, in which air temperatures dominate the influence. While the non-environmental factors, i.e. seasonal change, time of day and personal preference, also affects the patterns of window-opening probability. This paper provides solid field data on occupant window opening behavior in China, with high resolutions and demonstrates the way in analyzing and predicting the probability of window-opening behavior. Its discussion into the potential impact factors shall be useful for further investigation of the relationship between building energy consumption and window-opening behavior.

  • 96.
    Pourghadiri Esfahani, Mohammad
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Effect analysis of the 2008 charge increase of the nitrogen oxide on "Adoptive NOx Intensity" for Swedish pulp and paper industry2015Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The aim of this study is to evaluate the effectiveness of the NOx charge increase in 2008 on the behavior of NOx emissions from pulp and paper combustion plants, which are in the fee and refund system (regulated plants) in Sweden. In order to evaluate the potential change in NOx emissions behavior, this study used Difference-in-Differences (DID) approach. This involves a ‘control group’ (unregulated plants) and a ‘treatment group’ (regulated plants). The control group consists of smaller plants which are not included in the fee and refund system. The crudest DID assumption is that the average difference in emissions would have stayed constant over time for the control group and the treatment group if the increase of the fee would not have taken place. The results suggest that the NOx charge increase has had negative effects on NOx emission efficiency ("Adoptive NOx Intensity") for some years after 2008. However the reduction in NOx emission efficiency is inconsistent for the period over 2008-13. The difference between the control group and the treatment group jumps up and down in a way that is difficult to explain by the increase of the fee in 2008. This indicates that the DID assumption for the DID estimator may not be fulfilled. It is concluded that the results given these conditions are not reliable to draw any strong conclusions about the possible effect of the increase of the fee in 2008.

  • 97.
    Prochazka, Jiri
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Zerin, Sonia
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Modeling Hospital Readmissions in Dalarna County, Sweden2016Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Unplanned hospital readmissions increase health and medical care costs and indicate lower the lower quality of the healthcare services. Hence, predicting patients at risk to be readmitted is of interest. Using administrative data of patients being treated in the medical centers and hospitals in the Dalarna County, Sweden, during 2008 – 2016 two risk prediction models of hospital readmission are built. The first model relies on the logistic regression (LR) approach, predicts correctly 2,648 out of 3,392 observed readmission in the test dataset, reaching a c-statistics of 0.69. The second model is built using random forests (RF) algorithm; correctly predicts 2,183 readmission (out of 3,366) and 13,198 non-readmission events (out of 18,982). The discriminating ability of the best performing RF model (c-statistic 0.60) is comparable to that of the logistic model. Although the discriminating ability of both LR and RF risk prediction models is relatively modest, still these models are capable to identify patients running high risk of hospital readmission. These patients can then be targeted with specific interventions, in order to prevent the readmission, improve patients’ quality of life and reduce health and medical care costs.

  • 98.
    Rahman, Md Rezaur
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Joy, Frank
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Estimation of the causal effect of hospital outlier on patient outcomes: A case study of the hospital and patient care units inDalarna County, Sweden2018Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    An outlier patient is a medical or surgical patient who cannot get admitted to the designated ward or care unit due to the lack of bed occupancy or human resources, and the hospital transfers the patient to another ward or care unit. This study aims to compare and evaluate the outcome of being an outlier patient with a non-outlier patient. Region Dalarna (Lanstinget Dalarna) in Sweden provided data of two hospitals with 158734 cases for the years from 2014 to 2017. This observational casecontrol study used three types of matching techniques to create balanced data sets. Multivariate analysis with logistic regression was used to analyze the outcome of patients regarding mortality rate and unplanned readmission rate. Multiple linear regression was used to perform outcome analysis for the hospital length of stay of the outlier patients. Fisher’s exact test was used to evaluate the significance of mortality rate and unplanned readmission in 30 days. For the patient’s length of stay, the study used two independent t-test. Medical outlier patients did not get affected regarding unplanned readmission. In case of mortality, two of our matched datasets showed outlier patients did not have different mortality rate than non-outlier patients; only one matched dataset showed significance for mortality in case of outlier patients compared to the non-outlier patients. However, outlier patients had a significantly shorter duration of hospital stay than non-outlier patients in all three matched dataset.

  • 99.
    Raihana, Nishat
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Optimization of Operational Overhead based on the Evaluation of Current Snow Maintenance System: A Case Study of Borlänge, Sweden2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This study analyzes snow maintenance data of Borlänge municipality of Sweden based on the data of 2017 to 2018. The goal of this study is to reduce operational overhead of snow maintenance, for example, fuel and time consumption of the snow maintenance vehicles, work hour of dedicated personnel, etc. Borlänge Energy equipped the snow maintenance vehicles with GPS devices which stored the record of the snow maintenance activities. The initial part of this study obtained insights out of the GPS data by using spatiotemporal data analysis. Derivation of the different snow maintenance treatments (plowing, sanding and salting) as well as the efficiency of the sub-contractors (companies which are responsible for snow maintenance) and inspectors (personnel who are liable to call the subcontractors if they think it is time for snow maintenance) are performed in the beginning of this study. The efficiency of the subcontractors and inspectors are measured to compare their performance with each other.

    The latter part of this study discusses a simulated annealing-based heuristics technique to find out optimal location for dispatching snow maintenance vehicles. In the existing system of snow maintenance, drivers of the maintenance vehicles decide to start location of maintenance work based on their experience and intuition, which might vary from one driver to another driver. The vehicle dispatch locations are calculated based on the availability of the vehicles. For example, if a subcontractor has three vehicles to perform snow maintenance on a specific road map, the proposed solution would suggest three locations to dispatch those vehicles.

    The purpose of finding the optimal dispatch location is to reduce the total travel distance of the maintenance vehicles, which yield less fuel and time consumption. The study result shows the average travel distance for 1, 3, and 5 vehicles on 15 road networks. The proposed solution would yield 18% less travel than the existing system of snow maintenance.

  • 100.
    Rajasekaran, Prabakaran
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A statistical data analysis approach to Energy Data: A Case Study in Building Performance Analysis of Thermal Energy Loss2015Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The usage of energy in buildings is higher in Sweden’s cold climate, most buildings consume

    significant energy to heat buildings during the winter and cool the buildings during the summer, using

    the district heat and electricity. The building energy loss is the difference between indoor and outdoor

    temperatures. When the temperature difference is higher during the heating season (winter), there is

    a need to balance the indoor temperature. More power supply is needed to warm the indoor area. In

    order to find which factor has a significant impact on the building, heat loss and gain can be used as

    variables in the multiple linear regression model to analyze the building energy performance. In order

    to build the multiple – linear regression model which is used, the measured parameters for the building

    and some of the data should be calculated, such as solar heat gain through windows from the (North,

    East, West &South) building. The heat loss to the ground is based on constructed material, the thermal

    conductivity of the material, indoor and outdoor temperature, and steady state ground heat transfer

    coefficient. After building the model, an analysis of the fit model test is needed, to investigate if the

    coefficients are properly estimated. Based on this analysis, we can see the comparison between

    renovated building and non renovated building significant impact of the energy consumption for

    given the energy and financial investment.

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