Evaluation of the Impact of Family Planning Programs on

This article evaluates the fertility impact of family planning program by using prevalence model in Iran. Prevalence model, which introduced by John Bongaarts, estimates potential fertility and the number of births averted by program and non-program sources by using population and acceptor based data. The difference between potential fertility and observed fertility is related to contraception. The greater the differences between potential and observed fertility, the higher the impact of family planning program on fertility. The study uses the Base Line Survey-2001 (BLS-2001) data, collected by Statistical Center of Iran (SCI) and UNFPA-Iran in selected districts of Bushehr (Bushehr and Kangan Districts), Golestan (Gonbadkavoos and Minoodasht Districts), Kurdistan (Marivan and Divandareh Districts), Sistan & Bluchestan (Zahedan and Zabol Districts) and Tehran provinces (Islamshahr District). The results of the study indicate that Marivan and Zahedan districts had the high and low reduction rates in TFR and CBR, respectively. The findings also, show that the high reduction in ASFR belongs to age groups 30-34 in Marivan, 35-39 in Islamshahr, Gonbadkavoos and Bushehr, 40-44 in Zabol, Divandareh and Kangan districts and 45-49 in Zahedan and Minoodasht districts. In terms of each method contributions in reducing fertility, results show that the highest contribution of program contraceptives in preventing births in different districts are female sterilization in Bushehr, Divandareh and Islamshahr, and pill in other districts.Evaluation Research, Natural Fertility, Gross and Net Potential Fertility, Births averted, Prevalence model, Iran

Learning-based Predictive Control Approach for Real-time Management of Cyber-physical Systems

Cyber-physical systems (CPSs) are composed of heterogeneous, and networked hardware and software components tightly integrated with physical elements [72]. Large-scale CPSs are composed of complex components, subject to uncertainties [89], as though their design and development is a challenging task. Achieving reliability and real-time adaptation to changing environments are some of the challenges involved in large-scale CPSs development [51]. Addressing these challenges requires deep insights into control theory and machine learning. This research presents a learning-based control approach for CPSs management, considering their requirements, specifications, and constraints. Model-based control approaches, such as model predictive control (MPC), are proven to be efficient in the management of CPSs [26]. MPC is a control technique that uses a prediction model to estimate future dynamics of the system and generate an optimal control sequence over a prediction horizon. The main benefit of MPC in CPSs management comes from its ability to take the predictions of system’s environmental conditions and disturbances into account [26]. In this dissertation, centralized and distributed MPC strategies are designed for the management of CPSs. They are implemented for the thermal management of a CPS case study, smart building. The control goals are optimizing system efficiency (lower thermal power consumption in the building), and improving users’ convenience (maintaining desired indoor thermal conditions in the building). Model-based control strategies are advantageous in the management of CPSs due to their ability to provide system robustness and stability. The performance of a model-based controller strongly depends on the accuracy of the model as a representation of the system dynamics [26]. Accurate modeling of large-scale CPSs is difficult (due to the existence of unmodeled dynamics and uncertainties in the modeling process); therefore, modelbased control approach is not practical for these systems [6]. By incorporating machine learning with model-based control strategies, we can address CPS modeling challenges while preserving the advantages of model-based control methods. In this dissertation, a learning-based modeling strategy incorporated with a model-based control approach is proposed to manage energy usage and maintain thermal, visual, and olfactory performance in buildings. Neural networks (NNs) are used to learn the building’s performance criteria, occupant-related parameters, environmental conditions, and operation costs. Control inputs are generated through the model-based predictive controller and based on the learned parameters, to achieve the desired performance. In contrast to the existing building control systems presented in the literature, the proposed management system integrates current and future information of occupants (convenience, comfort, activities), building energy trends, and environment conditions (environmental temperature, humidity, and light) into the control design. This data is synthesized and evaluated in each instance of decision-making process for managing building subsystems. Thus, the controller can learn complex dynamics and adapt to the changing environment, to achieve optimal performance while satisfying problem constraints. Furthermore, while many prior studies in the filed are focused on optimizing a single aspect of buildings (such as thermal management), and little attention is given to the simultaneous management of all building objectives, our proposed management system is developed considering all buildings’ physical models, environmental conditions, comfort specifications, and occupants’ preferences, and can be applied to various building management applications. The proposed control strategy is implemented to manage indoor conditions and energy consumption in a building, simulated in EnergyPlus software. In addition, for comparison purposes, we designed and simulated a baseline controller for the building under the same conditions

Understanding collective international opportunity recognition : Studies on Finnish SMEs exploring maritime and offshore industry markets in Norway and Russia

This doctoral research was triggered by my interest in understanding why some Finnish maritime and offshore sector SMEs engage in a joint entry to new, challenging foreign markets, such as neighbouring Norway and Russia, while others do not. On this basis, the objective of the thesis is to explore the dynamics of the collective international opportunity recognition process among Finnish maritime and offshore industry SMEs that aim at joint internationalisation. This objective is divided into the following research questions: (1) How do individual entrepreneurs recognise collective international opportunities? (2) How do several entrepreneurs together recognise collective international opportunities? (3) How does the collective international opportunity recognition process evolve over time? These questions are answered in four empirical articles that constitute the core of the thesis. The employed theoretical framework builds on three streams of literature: international opportunity recognition, mental images and sensemaking, and network interaction. The qualitative data were collected primarily via biyearly interviews from 2015 to 2017 with representatives of Finnish maritime industry SMEs exploring business opportunities in Norway and Russia. The thesis contributes particularly to the international entrepreneurship literature by providing insight into collective international opportunity recognition, a process critical to the joint internationalisation of SMEs yet highly understudied. First, by introducing mental images specific to opportunity contexts and by exploring the dynamics of auspicious and ominous sensemaking involved in an individual’s recognition of collective international opportunities, this thesis sheds light on the individual-level aspects of the phenomenon. Second, by investigating two forms of inter-firm sensemaking, that is, collective and fragmented sensemaking, the study provides insight into how multiple individuals from different firms come to recognise, together, an opportunity for joint internationalisation. Third, by building on the process-based approach, this thesis provides understanding on the temporal dynamics of collective international opportunity recognition: mental images and sensemaking processes evolve over time through various kinds of events and determine whether managers recognize opportunities for joint internationalisation in the future. In addition, this thesis provides avenues for further research and offers managerial and policy recommendations for supporting the joint internationalisation of SMEs.Tämä väitöskirjatutkimus sai alkunsa kiinnostuksestani ymmärtää miksi jotkin suomalaiset meri- ja offshore-teollisuuden pk-yritykset lähtevät yhteistyössä kansainvälistymään uusille haastaville markkinoille, kuten Norjaan ja Venäjälle, kun taas toiset eivät. Väitöskirjan tavoitteena onkin tutkia kollektiivisten kansainvälistymismahdollisuuksien tunnistamisen dynamiikkaa sellaisten suomalaisten meri- ja offshore-teollisuuden pk-yritysten keskuudessa, jotka pyrkivät kansainvälisille markkinoille yhteistyössä. Tämä tavoite jakautuu edelleen seuraaviin tutkimuskysymyksiin: (1) Miten yksittäiset yrittäjät tunnistavat yhteisen kansainvälistymisen mahdollisuuksia? (2) Miten useat yrittäjät yhdessä tunnistavat yhteisen kansainvälistymisen mahdollisuuksia? (3) Miten yhteisten kansainvälistymismahdollisuuksien tunnistaminen kehittyy ajan kuluessa? Vastaan näihin kysymyksiin neljässä empiirisessä artikkelissa, jotka muodostavat väitöskirjan ytimen. Tutkimuksen teoreettinen viitekehys yhdistää elementtejä kolmesta tutkimuskirjallisuudesta, jotka ovat kansainvälisten mahdollisuuksien tunnistaminen,’mielikuvat’ (mental images) ja ’järkeistäminen’ (sensemaking), sekä vuorovaikutus yritysverkostoissa. Olen kerännyt tutkimuksen kvalitatiivisen aineiston haastattelemalla Norjan ja Venäjän liiketoimintamahdollisuuksia tutkailevien suomalaisten meriteollisuusyritysten edustajia puolivuosittain vuosina 2015–2017. Väitöskirja edistää erityisesti kansainvälisen yrittäjyyden kirjallisuutta luomalla uutta ymmärrystä kollektiivisten kansainvälisten mahdollisuuksien tunnistamisesta, joka on pk-yritysten kansainvälistymiselle kriittinen, mutta silti vielä varsin tutkimaton ilmiö. Ensinnäkin, tutkimuksen mukaan eri mahdollisuuskonteksteihin liittyvät mielikuvat sekä hyväenteinen ja pahaenteinen järkeistäminen ovat keskeisiä yksilötason elementtejä yhteisten kansainvälisten mahdollisuuksien tunnistamisessa. Toiseksi, yritystenvälisellä tasolla hajautununut ja kollektiivinen järkeistäminen puolestaan määrittävät miten useat yksilöt yhdessä tunnistavat mahdollisuuksia kansainvälistyä yhteistyössä. Kolmanneksi, tämä prosessipohjainen tutkimus avaa ilmiön ajallista dynamiikkaa; miten mielikuvat ja järkeistäminen kehittyvät erilaisten tapahtumien myötä ja vaikuttavat siihen miten johtajat tunnistavat yhteisen kansainvälistymisen mahdollisuuksia tulevaisuudessa. Tutkimus tarjoaa myös jatkotutkimusehdotuksia sekä suosituksia pk-yritysten yhteisen kansainvälistymisen tukemiseksi