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Purpose: E-tourism is the most cost-effective way to communicate with the target market and to publish information. It is also an easy way for customers to buy travel products. Recent advances in mobile communication technologies are leading the way for the next generation of travel commercial applications. Location-based services are among these facilities available on the mobile platform. These services use mobile tracking techniques to provide users with relevant information at the right time, based on their current location. This study investigates the LBSs value in tourism and their effect on destination brand equity components and tourist behavior. Method: The sample of the study totaled 210 visitors from Isfahan as one of the popular tourist cities in the world. The research hypotheses were analyzed using Smart-PLS3 software. Findings: The research findings revealed that LBSs have positive and significant effects on destination brand image, destination brand loyalty and WOM. Conclusion: As this paper is among the first studies investigating the positive effect of LBS on destination brand equity components and tourist behavior, the findings are beneficial for DMOs and their marketing managers. Due to their potential to create a unique travel experience as well as to personalize and tailor tourism services based on tourists' needs and personal preferences, LBS provide them with significant added value.

Sleep quality is an important determinant of human health and wellbeing and impacts on many aspects of health, ranging from everyday fitness and general alertness to the rate of recovery from a serious illness. Two important indicators of sleep quality are body posture and movements during sleep. Currently, clinical diagnosis of sleep disorders such as sleep apnea or rapid eye movement (REM) sleep behavior disorder (RBD) requires patients to undergo a polysomnography (PSG)-based assessment in a dedicated clinical sleep unit. This involves attaching multiple electrodes to the head and body, which can themselves impact on sleep quality. Additionally, the current standard manual or automated scoring of sleep state lacks a comprehensive quantification of body position during sleep., However, non-contact camera-based sleep monitoring offers an alternative approach to sleep quality assessment that addresses these shortcomings. Nevertheless, these approaches have not been validated against the PSG gold standard or other clinical methods. Such evaluations to date have relied mainly on simulated sleep rather than natural sleep or require subjects to avoid any occlusion from the bedding. These aspects constitute significant limitations for the routine implementation of video monitoring to measure body position and movements during sleep., In this thesis, the design and development of a non-contact monitoring system that automatically analyses body posture and movement by using video data captured from actual sleep using a blanket bed covering are presented. Experimental data are compared to gold standard methods of PSG and manual expert annotation of the video data. Starting with simulated sleep data, a variety of different methodologies were explored based on deep learning for automatic sleep pose detection, including a 4-layer convolutional neural network (CNN) network, two-step deep learning, and combining deep learning with tensor factorisation for the detection of body poses during sleep when poses were occluded by a blanket., Nocturnal sleep was quantified in 12 healthy participants using recordings of the IR camera as well as PSG data in the Surrey sleep laboratory. Using a transfer learning approach applied to IR camera data, supervised machine learning strategies successfully quantified sleep poses of participants covered by a blanket. This represents the first occasion that such a machine learning approach has been used to successfully detect four predefined poses and the empty bed state during 8-10 hour overnight sleep episodes. The Markov Chain transition matrix was also used to quantify sleep behaviour and perform another comparison method against PSG, and manual sleep pose scoring. In a cohort of 12 healthy participants, we found that fine-tuning a ResNet-152 pre-trained CNN network achieved the best performance compared with the standard end-to-end CNN and other pre-trained CNN networks. The method outperforms other video-based methods with an accuracy of 95.1% for sleep pose estimation and outperforms the clinical standard for pose estimation using a PSG position sensor., Unsupervised data-driven pose analysis has also been investigated as a potential avenue of quantifying personalised sleep behaviour. This approach revealed that a participant may have as many as 17 distinct sleep poses during a nocturnal sleep episode. Pilot analysis of the correlation between the sleep poses as well as movement with sleep physiology such as sleep stage, heart rate, and heart rate variability has also been performed. This demonstrates that while sleep pose based on four standard poses and sleep physiology only show low levels of correlation, unsupervised clustering when combined with physiology such as heart rate may offer an alternative approach to resolving sleep states using non-contact strategies. The results of these studies indicate that it is feasible to use video data and machine learning to quantify sleep behaviour.

I 2019 ble endringer i læreplaner for grunnskole og videregående skole vedtatt av Kunnskapsdepartementet, og de nye læreplanene ble tatt i bruk fra og med skolestart høsten 2020. Ifølge Kunnskapsdepartementet (2019), med de nye læreplanene får skolen et verdiløft. Dette blir den største endringen av skolens innhold siden Kunnskapsløftet i 2006. Endringer i de nye læreplanene skal bidra til at elever får et bedre grunnlag for kritisk tenkning, utforskning og kreativitet. Som følge av disse endringene, skal lærere sette fokus på begreper som algoritmisk tenkning, problemløsnings strategier og programmering (Stenlund, 2020). Disse gjelder spesieltvmatematikkundervisning (Utdanningsdirektoratet, 2020). I forbindelse med fagfornyelsen spiller matematikkfaget en nøkkelrolle i programmerings opplæring, på grunn av programmering benytter matematikk på mange måter, og i flere områder (Bueie, 2019). Innføringen av programmering i skolen har skapt noen utfordringer hos lærere i tjenesten (Stenlund, 2020). Lærernes behov for programmerings kunnskap, har ført til at flere universiteter og høyskoler startet å tilby etterutdannings opplæring i programmering for lærere. Lærere har også vært aktive til å delta i slike kurs for å lære å anvende programmering i sine fag. Til tross for slike tilbud, er fremdeles en utfordring for lærere å integrere programmering i deres undervisnings plan. Det ser derfor nødvendig ut for en klassifisert samling av matematiske oppgaver som er også i samsvar med de nye læreplanene for matematikkfaget på VSG. For å løse problemet er et Design Science Research (DSR) prosjekt gjennomført. Innsatsen med prosjektet er å utforske anvendelse av programmering i matematikk, og utvikle matematiske algoritmer og programkoder for å lage en klassifisert samling av matematiske oppgaver som hjelpemiddel for matematikklærere i tjenesten. Som resultat av prosjektet er til sammen 37 oppgaver samlet inn under et hefte. Disse oppgavene tilhører matematikkfagene 1P, 1T, 2P, R1, R2, S1 og S2 på VGS. For enhver oppgave utarbeides løsnings strategien, algoritmen, og programkoden. Algoritmen i hver oppgave viser skrittvis hvordan problemet kan programmeres. Hoved temaer i de valgte oppgavene er blant de temaene som er understreket i de nye læreplanene som for eksempel problemløsning, algoritmisk tenkning, og programmering. Det er også forsøkt å velge de oppgavene som regnes som interessante, engasjerende og viktige matematiske oppgaver med tanke på å stimulere kreativitet hos elevene, og hjelpe dem til å bli gode problemløsere. In 2019, changes in curricula for primary and secondary school were adopted by the Ministry of Education, and the new curricula were implemented from the start of school in the autumn of 2020. According to the Ministry of Education, with the new curricula, the school gets a boost in value. This will be the largest change in the school's content since the Knowledge Promotion in 2006. Changes in the new curricula will help pupils to have a better basis for critical thinking, exploration, and creativity (Kunnskapsdepartementet, 2019). As a result of these changes, teachers will focus on concepts such as algorithmic thinking, problem-solving strategies, and programming (Stenlund, 2020). These are especially applied to mathematics teaching (Utdanningsdirektoratet, 2020). In connection with the education reform, the subject of mathematics plays a key role in programming education, due to programming uses mathematics in many ways, and in several areas (Bueie, 2019). The introduction of programming in schools has created some challenges for in-service teachers (Stenlund, 2020). The teachers' need for programming knowledge has led to several universities and colleges starting to offer continuing education courses in programming for teachers. Teachers have also been active in attending such courses to learn how to apply programming in their subjects. Despite such offerings, it is still a challenge for teachers to integrate programming into their curriculum. Therefore, it seems necessary for a classified collection of mathematical exercises that is also in accordance with the new curricula for the mathematics subject at Norwegian upper secondary schools (USS). To solve the problem, a Design Science Research (DSR) project has been completed. The effort with the project is to search the use of programming in mathematics, and develop mathematical algorithms and program codes to create a classified collection of mathematical problems as an aid for mathematics teachers in the service. As a result of the project, a total of 37 exercises have been collected under a booklet. These exercises belong to the mathematics subjects 1P, 1T, 2P, R1, R2, S1 and S2 at UUS. For each task, the solution strategy, algorithm, and program code are prepared. The algorithm in each task shows step by step how the problem can be programmed. The main subjects in the selected exercises are among the subjects that are emphasized in the new curricula, such as problem-solving, algorithmic thinking, and programming. It is also attempted to select the tasks that are considered interesting, engaging, and important mathematical tasks with a view to, stimulating the creativity of the pupils, and helping them to become good problem-solvers.

Background: The systemic inflammatory cascade triggered in donors after brain death enhances the ischemia-reperfusion injury after organ transplantation. Intravenous steroids are routinely used in the intensive care units for the donor preconditioning. Immunosuppressive medications could be potentially used for this purpose as well. Data regarding donor preconditioning with calcineurin inhibitors or inhibitors of mammalian target for Rapamycin is limited. The aim of this project is to investigate the effects of (oral) donor preconditioning with a calcineurin inhibitor (Cyclosporine) vs. an inhibitor of mammalian target for Rapamycin (Everolimus) compared to the conventional administration of steroid in the setting of donation after brain death in porcine renal transplantation. Methods: Six hours after the induction of brain death, German landrace donor pigs (33.2 ± 3.9 kg) were randomly preconditioned with either Cyclosporine (n = 9) or Everolimus (n = 9) administered via nasogastric tube with a repeated dose just before organ procurement. Control donors received intravenous Methylprednisolone (n = 8). Kidneys were procured, cold-stored in Histidine-Tryptophane-Ketoglutarate solution at 4°C and transplanted in nephrectomized recipients after a mean cold ischemia time of 18 h. No post-transplant immunosuppression was given to avoid confounding bias. Blood samples were obtained at 4 h post reperfusion and daily until postoperative day 5 for complete blood count, blood urea nitrogen, creatinine, and electrolytes. Graft protocol biopsies were performed 4 h after reperfusion to assess early histological and immunohistochemical changes. Results: There was no difference in the hemodynamic parameters, hemoglobin/hematocrit and electrolytes between the groups. Serum blood urea nitrogen and creatinine peaked on postoperative day 1 in all groups and went back to the preoperative levels at the conclusion of the study on postoperative day 5. Histological assessment of the kidney grafts revealed no significant differences between the groups. TNF-α expression was significantly lower in the study groups compared with Methylprednisolone group (p = 0.01) Immunohistochemistry staining for cytochrome c showed no difference between the groups. Conclusion: Oral preconditioning with Cyclosporine or Everolimus is feasible in donation after brain death pig kidney transplantation and reduces the expression of TNF-α. Future studies are needed to further delineate the role of oral donor preconditioning against ischemia-reperfusion injury.