12,498 research outputs found

    The LDBC social network benchmark: Business intelligence workload

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    The Social Network Benchmark’s Business Intelligence workload (SNB BI) is a comprehensive graph OLAP benchmark targeting analytical data systems capable of supporting graph workloads. This paper marks the finalization of almost a decade of research in academia and industry via the Linked Data Benchmark Council (LDBC). SNB BI advances the state-of-the art in synthetic and scalable analytical database benchmarks in many aspects. Its base is a sophisticated data generator, implemented on a scalable distributed infrastructure, that produces a social graph with small-world phenomena, whose value properties follow skewed and correlated distributions and where values correlate with structure. This is a temporal graph where all nodes and edges follow lifespan-based rules with temporal skew enabling realistic and consistent temporal inserts and (recursive) deletes. The query workload exploiting this skew and correlation is based on LDBC’s “choke point”-driven design methodology and will entice technical and scientific improvements in future (graph) database systems. SNB BI includes the first adoption of “parameter curation” in an analytical benchmark, a technique that ensures stable runtimes of query variants across different parameter values. Two performance metrics characterize peak single-query performance (power) and sustained concurrent query throughput. To demonstrate the portability of the benchmark, we present experimental results on a relational and a graph DBMS. Note that these do not constitute an official LDBC Benchmark Result – only audited results can use this trademarked term

    Multimodal spatio-temporal deep learning framework for 3D object detection in instrumented vehicles

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    This thesis presents the utilization of multiple modalities, such as image and lidar, to incorporate spatio-temporal information from sequence data into deep learning architectures for 3Dobject detection in instrumented vehicles. The race to autonomy in instrumented vehicles or self-driving cars has stimulated significant research in developing autonomous driver assistance systems (ADAS) technologies related explicitly to perception systems. Object detection plays a crucial role in perception systems by providing spatial information to its subsequent modules; hence, accurate detection is a significant task supporting autonomous driving. The advent of deep learning in computer vision applications and the availability of multiple sensing modalities such as 360° imaging, lidar, and radar have led to state-of-the-art 2D and 3Dobject detection architectures. Most current state-of-the-art 3D object detection frameworks consider single-frame reference. However, these methods do not utilize temporal information associated with the objects or scenes from the sequence data. Thus, the present research hypothesizes that multimodal temporal information can contribute to bridging the gap between 2D and 3D metric space by improving the accuracy of deep learning frameworks for 3D object estimations. The thesis presents understanding multimodal data representations and selecting hyper-parameters using public datasets such as KITTI and nuScenes with Frustum-ConvNet as a baseline architecture. Secondly, an attention mechanism was employed along with convolutional-LSTM to extract spatial-temporal information from sequence data to improve 3D estimations and to aid the architecture in focusing on salient lidar point cloud features. Finally, various fusion strategies are applied to fuse the modalities and temporal information into the architecture to assess its efficacy on performance and computational complexity. Overall, this thesis has established the importance and utility of multimodal systems for refined 3D object detection and proposed a complex pipeline incorporating spatial, temporal and attention mechanisms to improve specific, and general class accuracy demonstrated on key autonomous driving data sets

    Optimal Transmit Power and Channel-Information Bit Allocation With Zeroforcing Beamforming in MIMO-NOMA and MIMO-OMA Downlinks

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    In downlink, a base station (BS) with multiple transmit antennas applies zeroforcing beamforming to transmit to single-antenna mobile users in a cell. We propose the schemes that optimize transmit power and the number of bits for channel direction information (CDI) for all users to achieve the max-min signal-to-interference plus noise ratio (SINR) fairness. The optimal allocation can be obtained by a geometric program for both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA). For NOMA, 2 users with highly correlated channels are paired and share the same transmit beamforming. In some small total-CDI rate regimes, we show that NOMA can outperform OMA by as much as 3 dB. The performance gain over OMA increases when the correlation-coefficient threshold for user pairing is set higher. To reduce computational complexity, we propose to allocate transmit power and CDI rate to groups of multiple users instead of individual users. The user grouping scheme is based on K-means over the user SINR. We also propose a progressive filling scheme that performs close to the optimum, but can reduce the computation time by almost 3 orders of magnitude in some numerical examples

    Time-varying STARMA models by wavelets

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    The spatio-temporal autoregressive moving average (STARMA) model is frequently used in several studies of multivariate time series data, where the assumption of stationarity is important, but it is not always guaranteed in practice. One way to proceed is to consider locally stationary processes. In this paper we propose a time-varying spatio-temporal autoregressive and moving average (tvSTARMA) modelling based on the locally stationarity assumption. The time-varying parameters are expanded as linear combinations of wavelet bases and procedures are proposed to estimate the coefficients. Some simulations and an application to historical daily precipitation records of Midwestern states of the USA are illustrated

    Small newborns in post-conflict Northern Uganda: Burden and interventions for improved outcomes

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    Introduction: A small newborn can be the result of either a low birthweight (LBW), or a preterm birth (PB), or both. LBW can be due to either a preterm appropriate-for gestational-age (preterm-AGA), or a term small-for-gestational age (term-SGA) or intrauterine growth restriction (IUGR). An IUGR is a limited in-utero foetal growth rates or foetal weight < 10th percentile. Small newborns have an increased risk of dying, particularly in low-resource settings. We set out to assess the burden, the modifiable risk factors and health outcomes of small newborns in the post-conflict Northern Ugandan district of Lira. In addition, we studied the use of video-debriefing when training health staff in Helping Babies Breathe. Subjects and methods: In 2018-19, we conducted a community-based cohort study on 1556 mother-infant dyads, nested within a cluster randomized trial. In our cohort study, we estimated the incidence and risk factors for LBW and PB and the association of LBW with severe outcomes. We explored the prevalence of and factors associated with neonatal hypoglycaemia, as well as any association between neonatal death and hypoglycaemia. In addition, we conducted a cluster randomized trial to compare Helping Babies Breathe (HBB) training in combination with video debriefing to the traditional HBB training alone on the attainment and retention of health worker neonatal resuscitation competency. Results: The incidence of LBW and PB in our cohort was lower than the global estimates, 7.3% and 5.0%, respectively. Intermittent preventive treatment for malaria was associated with a reduced risk of LBW. HIV infection was associated with an increased risk of both LBW and PB, while maternal formal education (schooling) of ≄7 years was associated with a reduced risk of LBW and PB. The proportions of neonatal deaths were many-folds higher among LBW infants compared to their non-LBW counterparts. The proportion of neonatal deaths among LBW was 103/1000 live births compared to 5/1000 among the non-LBW. The prevalence of neonatal hypoglycaemia in our cohort was 2.5%. LBW and PB each independently were associated with an increased risk of neonatal hypoglycaemia. Neonatal hypoglycaemia was associated with an increased risk of hospitalisation and severe outcomes. We demonstrated that neonatal resuscitation training with video debriefing, improved competence attainment and retention among health workers, compared to traditional HBB training alone. Conclusion: In northern Uganda, small infants still have a many-fold higher risk of dying compared to normal infants. In addition, small infants are also at more risk of neonatal hypoglycaemia compared to normal infants. Efforts are needed to secure essential newborn care, should we reach the target of Sustainable Development Goal number 3.2 of reducing infant mortality to less than 12/1000 live births by 2030

    Évaluation de l'impact du changement climatique sur la dĂ©foliation de l'Ă©pinette noire par la tordeuse des bourgeons de l'Ă©pinette

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    Les modĂšles Ă©cologiques actuels prĂ©voient de profonds effets des changements climatiques sur les rĂ©gimes de perturbations naturelles des forĂȘts. La tordeuse des bourgeons de l'Ă©pinette (Choristoneura fumiferana) (TBE) est le principal insecte dĂ©foliateur dans l'est de l'AmĂ©rique du Nord. Les Ă©pidĂ©mies de TBE ont un impact majeur sur la structure et la fonction de la forĂȘt borĂ©ale canadienne puisque la dĂ©foliation entraĂźne une diminution de la croissance des arbres, une augmentation de la mortalitĂ© et une baisse de la productivitĂ© forestiĂšre. Les Ă©pidĂ©mies de TBE sont devenues plus sĂ©vĂšres au cours du dernier siĂšcle Ă  cause des changements climatiques; cependant, nous savons peu de choses sur la maniĂšre dont l'effet intĂ©grĂ© du climat et du TBE modifie la croissance des espĂšces hĂŽtes. Nous Ă©valuons ici comment l’interaction entre le climat et la gravitĂ© de l'Ă©pidĂ©mie affecte la croissance de l'Ă©pinette noire (Picea mariana) pendant l'Ă©pidĂ©mie de TBE qui a eu lieu entre 1968-1988 et 2006-2017. Nous avons compilĂ© des sĂ©ries dendrochronologiques (2271 arbres), des donnĂ©es de sĂ©vĂ©ritĂ© de l'Ă©pidĂ©mie (estimĂ©e par la dĂ©foliation aĂ©rienne observĂ©e) et des donnĂ©es climatiques pour 164 sites au QuĂ©bec, Canada. Nous avons utilisĂ© un modĂšle linĂ©aire Ă  effets mixtes pour dĂ©terminer l'impact des paramĂštres climatiques, de la dĂ©foliation cumulative (des cinq annĂ©es prĂ©cĂ©dentes) et de leur effet couplĂ© sur la croissance en surface terriĂšre. À la gravitĂ© maximale de l'Ă©pidĂ©mie, la croissance en surface terriĂšre de l'Ă©pinette noire a Ă©tĂ© rĂ©duite de 14 Ă  18 % sur les cinq annĂ©es en raison de l'effet TBE. Cette croissance a Ă©tĂ© affectĂ©e par le climat : des tempĂ©ratures minimales estivales prĂ©cĂ©dentes plus Ă©levĂ©es et un indice d'humiditĂ© climatique estival plus Ă©levĂ© ont rĂ©duit la croissance de 11 % et 4 % respectivement. En revanche, l'effet nĂ©gatif de la dĂ©foliation a Ă©tĂ© attĂ©nuĂ© de 9% pour une tempĂ©rature minimale plus Ă©levĂ©e au printemps prĂ©cĂ©dent et de 7% pour une tempĂ©rature maximale plus Ă©levĂ©e l'Ă©tĂ© prĂ©cĂ©dent. Cette Ă©tude amĂ©liore notre comprĂ©hension des effets combinĂ©s de la TBE et du climat et aide Ă  prĂ©voir les dommages futurs causĂ©s par cet insecte dans les peuplements forestiers afin de soutenir la gestion durable des forĂȘts. Nous recommandons Ă©galement que les projections des Ă©cosystĂšmes dans la forĂȘt borĂ©ale incluent plusieurs classes de dĂ©foliation de la TBE et plusieurs scĂ©narios climatiques

    Reinforcement Learning-based User-centric Handover Decision-making in 5G Vehicular Networks

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    The advancement of 5G technologies and Vehicular Networks open a new paradigm for Intelligent Transportation Systems (ITS) in safety and infotainment services in urban and highway scenarios. Connected vehicles are vital for enabling massive data sharing and supporting such services. Consequently, a stable connection is compulsory to transmit data across the network successfully. The new 5G technology introduces more bandwidth, stability, and reliability, but it faces a low communication range, suffering from more frequent handovers and connection drops. The shift from the base station-centric view to the user-centric view helps to cope with the smaller communication range and ultra-density of 5G networks. In this thesis, we propose a series of strategies to improve connection stability through efficient handover decision-making. First, a modified probabilistic approach, M-FiVH, aimed at reducing 5G handovers and enhancing network stability. Later, an adaptive learning approach employed Connectivity-oriented SARSA Reinforcement Learning (CO-SRL) for user-centric Virtual Cell (VC) management to enable efficient handover (HO) decisions. Following that, a user-centric Factor-distinct SARSA Reinforcement Learning (FD-SRL) approach combines time series data-oriented LSTM and adaptive SRL for VC and HO management by considering both historical and real-time data. The random direction of vehicular movement, high mobility, network load, uncertain road traffic situation, and signal strength from cellular transmission towers vary from time to time and cannot always be predicted. Our proposed approaches maintain stable connections by reducing the number of HOs by selecting the appropriate size of VCs and HO management. A series of improvements demonstrated through realistic simulations showed that M-FiVH, CO-SRL, and FD-SRL were successful in reducing the number of HOs and the average cumulative HO time. We provide an analysis and comparison of several approaches and demonstrate our proposed approaches perform better in terms of network connectivity

    Die akute Appendizitis im Kindes- und Jugendalter: neue diagnostische Verfahren fĂŒr die prĂ€therapeutische Differenzierung histopathologischer EntitĂ€ten zur UnterstĂŒtzung konservativer Therapiestrategien

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    Hintergrund der hier zusammengefassten Studien war die aktuelle Datenlage, die dafĂŒr spricht, dass es sich bei der klinisch unkomplizierten, histopathologisch phlegmonösen und der klinisch komplizierten, histopathologisch gangrĂ€nösen Appendizitis um unabhĂ€ngige EntitĂ€ten handelt. Diese können unterschiedlichen Therapieoptionen (konservativ vs. operativ) zugefĂŒhrt werden. Vor diesem Hintergrund war es ein Ziel der Arbeiten zu untersuchen, wie die Formen der akuten Appendizitis im Kindes- und Jugendalter bereits prĂ€therapeutisch unterschieden werden können. Sowohl in der Labordiagnostik (P1 und P2) als auch im Ultraschall (P3) lassen sich Unterschiede zwischen Patient*innen mit unkomplizierter, phlegmonöser und komplizierter (gangrĂ€nöser und perforierender) Appendizitis aufzeigen. Hierdurch allein kann allerdings aufgrund unzureichender TrennschĂ€rfe noch keine ausreichende Entscheidungssicherheit erreicht werden. Mit Verfahren der kĂŒnstlichen Intelligenz auf Untersucher-unabhĂ€ngige diagnostische Parameter (P4) konnte die Vorhersagegenauigkeit der akuten Appendizitis weiter gesteigert werden. Interessante Ergebnisse bezĂŒglich der unterschiedlichen Pathomechanismen der beiden inflammatorischen EntitĂ€ten ergaben sich durch eine differenzielle Genexpressionsanalyse (P5). In einer Proof-of-Concept-Studie wurden zuvor beschriebene Methoden der kĂŒnstlichen Intelligenz auf die Genexpressionsdaten angewandt (P6). Hierdurch konnte im Modell eine grundsĂ€tzliche Differenzierbarkeit der EntitĂ€ten durch die Anwendung der neuen Methode aufgezeigt werden. Ein mittelfristiges Ziel ist es, eine Biomarkersignatur zu definieren, die ihre Aussagekraft durch einen Computeralgorithmus hat. Hierdurch soll eine schnelle Therapieentscheidung ermöglicht werden. Im Idealfall sollte diese Biomarkersignatur sicher, objektiv und einfach zu bestimmen sein sowie eine höhere diagnostische Sicherheit als die bisherige Diagnostik mittels Anamnese, Untersuchung, Laboranalyse und Ultraschall bieten. Langfristiges Ziel von Folgestudien ist die Identifizierung einer Biomarkersignatur mit der bestmöglichen Vorhersagekraft. Hinsichtlich der routinemĂ€ĂŸigen klinischen Diagnostik ist die Anwendung von Point-of-Care Devices auf PCR-Basis denkbar. Hier könnte eine limitierte Anzahl von Primern fĂŒr eine Biomarkersignatur mit hoher Vorhersagekraft zum Einsatz kommen. Der dadurch ermittelte Biomarker wĂŒrde seine Aussagekraft durch einen einfach anzuwendenden Computeralgorithmus erhalten. Die Kombination aus Genexpressionsanalyse mit Methoden der kĂŒnstlichen Intelligenz kann somit die Grundlage fĂŒr ein neues diagnostisches Instrument zur sicheren Unterscheidung unterschiedlicher AppendizitisentitĂ€ten darstellen

    Norsk rÄ kumelk, en kilde til zoonotiske patogener?

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    The worldwide emerging trend of eating “natural” foods, that has not been processed, also applies for beverages. According to Norwegian legislation, all milk must be pasteurized before commercial sale but drinking milk that has not been heat-treated, is gaining increasing popularity. Scientist are warning against this trend and highlights the risk of contracting disease from milkborne microorganisms. To examine potential risks associated with drinking unpasteurized milk in Norway, milk- and environmental samples were collected from dairy farms located in south-east of Norway. The samples were analyzed for the presence of specific zoonotic pathogens; Listeria monocytogenes, Campylobacter spp., and Shiga toxin-producing Escherichia coli (STEC). Cattle are known to be healthy carriers of these pathogens, and Campylobacter spp. and STEC have a low infectious dose, meaning that infection can be established by ingesting a low number of bacterial cells. L. monocytogenes causes one of the most severe foodborne zoonotic diseases, listeriosis, that has a high fatality rate. All three pathogens have caused milk borne disease outbreaks all over the world, also in Norway. During this work, we observed that the prevalence of the three examined bacteria were high in the environment at the examined farms. In addition, 7% of the milk filters were contaminated by STEC, 13% by L. monocytogenes and 4% by Campylobacter spp. Four of the STEC isolates detected were eaepositive, which is associated with the capability to cause severe human disease. One of the eae-positive STEC isolates were collected from a milk filter, which strongly indicate that Norwegian raw milk may contain potential pathogenic STEC. To further assess the possibilities of getting ill by STEC after consuming raw milk, we examined the growth of the four eae-positive STEC isolates in raw milk at different temperatures. All four isolates seemed to have ability to multiply in raw milk at 8°C, and one isolate had significant growth after 72 hours. Incubation at 6°C seemed to reduce the number of bacteria during the first 24 hours before cell death stopped. These findings highlight the importance of stable refrigerator temperatures, preferable < 4°C, for storage of raw milk. The L. monocytogenes isolates collected during this study show genetic similarities to isolates collected from urban and rural environmental locations, but different clones were predominant in agricultural environments compared to clinical and food environments. However, the results indicate that the same clone can persist in a farm over time, and that milk can be contaminated by L. monocytogenes clones present in farm environment. Despite testing small volumes (25 mL) of milk, we were able to isolate both STEC and Campylobacter spp. directly from raw milk. A proportion of 3% of the bulk tank milk and teat milk samples were contaminated by Campylobacter spp. and one STEC was isolated from bulk tank milk. L monocytogenes was not detected in bulk tank milk, nor in teat milk samples. The agricultural evolvement during the past decades have led to larger production units and new food safety challenges. Dairy cattle production in Norway is in a current transition from tie-stall housing with conventional pipeline milking systems, to modern loose housing systems with robotic milking. The occurrence of the three pathogens in this project were higher in samples collected from farms with loose housing compared to those with tiestall housing. Pasteurization of cow’s milk is a risk reducing procedure to protect consumers from microbial pathogens and in most EU countries, commercial distribution of unpasteurized milk is legally restricted. Together, the results presented in this thesis show that the animal housing may influence the level of pathogenic bacteria in the raw milk and that ingestion of Norwegian raw cow’s milk may expose consumers to pathogenic bacteria which can cause severe disease, especially in children, elderly and in persons with underlying diseases. The results also highlight the importance of storing raw milk at low temperatures between milking and consumption.Å spise mat som er mindre prosessert og mer «naturlig» er en pĂ„gĂ„ende trend i Norge og i andre deler av verden. Interessen for Ă„ drikke melk som ikke er varmebehandlet, sĂ„kalt rĂ„ melk, er ogsĂ„ Ăžkende. I Norge er det pĂ„budt Ă„ pasteurisere melk fĂžr kommersielt salg for Ă„ beskytte forbrukeren mot sykdomsfremkallende mikroorganismer. Fagfolk advarer mot Ă„ drikke rĂ„ melk, og pĂ„peker risikoen for Ă„ bli syk av patogene bakterier som kan finnes i melken. I denne avhandlingen undersĂžker vi den potensielle risikoen det medfĂžrer Ă„ drikke upasteurisert melk fra Norge. I tillegg til Ă„ samle inn tankmelk- og speneprĂžver fra melkegĂ„rder i sĂžrĂžst Norge, samlet vi ogsĂ„ miljĂžprĂžver fra de samme gĂ„rdene for Ă„ kartlegge forekomst og for Ă„ identifisere potensielle mattrygghetsrisikoer i melkeproduksjonen. Alle prĂžvene ble analysert for de zoonotiske sykdomsfremkallende bakteriene Listeria monocytogenes, Campylobacter spp., og Shiga toksin-produserende Escherichia coli (STEC). Kyr kan vĂŠre friske smittebĂŠrere av disse bakteriene, som dermed kan etablere et reservoar pĂ„ gĂ„rdene. Bakteriene kan overfĂžres fra gĂ„rdsmiljĂžet til melkekjeden og dermed utfordre mattryggheten. Disse bakteriene har forĂ„rsaket melkebĂ„rne sykdomsutbrudd over hele verden, ogsĂ„ i Norge. Campylobacter spp. og STEC har lav infeksiĂžs dose, som vil si at man kan bli syk selv om man bare inntar et lavt antall bakterieceller. L. monocytogenes kan gi sykdommen listeriose, en av de mest alvorlige matbĂ„rne zoonotiske sykdommene vi har i den vestlige verden. Resultater fra denne oppgaven viser en hĂžy forekomst av de tre patogenene i gĂ„rdsmiljĂžet. I tillegg var 7% av melkefiltrene vi testet positive for STEC, 13% positive for L. monocytogenes og 4% positive for Campylobacter spp.. Fire av STEC isolatene bar genet for Intimin, eae, som er ansett som en viktig virulensfaktor som Ăžker sjansen for alvorlig sykdom. Ett av de eae-positive isolatene ble funnet i et melkefilter, noe som indikerer at norsk rĂ„ melk kan inneholde patogene STEC. For Ă„ videre vurdere risikoen for Ă„ bli syk av STEC fra rĂ„ melk undersĂžkte vi hvordan de fire eae-positive isolatene vokste i rĂ„ melk lagret ved forskjellige temperaturer. For alle isolatene Ăžkte antall bakterier etter lagring ved 8°C, og for et isolat var veksten signifikant. Etter lagring ved 6°C ble antallet bakterier redusert de fĂžrste 24 timene, deretter stoppet reduksjonen i antall bakterier. Disse resultatene viser hvor viktig det er Ă„ ha stabil lav lagringstemperatur for rĂ„ melk, helst < 4°C. L. monocytogenes isolatene som ble samlet inn fra melkegĂ„rdene viste genetiske likheter med isolater samlet inn fra urbane og rurale miljĂžer rundt omkring i Norge. Derimot var kloner som dominerte i landbruksmiljĂžet forskjellige fra kliniske isolater og isolater fra matproduksjonslokaler. Videre sĂ„ man at en klone kan persistere pĂ„ en gĂ„rd over tid og at melk kan kontamineres av L. monocytogenes kloner som er til stede i gĂ„rdsmiljĂžet. Til tross for smĂ„ testvolum av tankmelken (25 mL) fant vi bĂ„de STEC og Campylobacter spp. i melkeprĂžvene. 3% av tankmelkprĂžvene og speneprĂžvene var positive for Campylobacter spp. og ett STEC isolat ble funnet i tankmelk. L. monocytogenes ble ikke funnet direkte i melkeprĂžvene. Landbruket i Norge er i stadig utvikling der besetningene blir stĂžrre, men fĂŠrre. Melkebesetningene er midt i en overgang der tradisjonell oppstalling med melking pĂ„ bĂ„s byttes ut med lĂžsdriftssystemer og melkeroboter. Forekomsten av de tre patogenene funnet i denne studien var hĂžyere i besetningene med lĂžsdrift sammenliknet med besetningene som hadde melkekyrne oppstallet pĂ„ bĂ„s. Pasteurisering er et viktig forebyggende tiltak for Ă„ beskytte konsumenter fra mikrobielle patogener, og i de fleste EU-land er kommersielt salg av rĂ„ melk juridisk begrenset. Denne studien viser at oppstallingstype kan pĂ„virke nivĂ„ene av patogene bakterier i gĂ„rdsmiljĂžet og i rĂ„ melk. Inntak av rĂ„ melk kan eksponere forbruker for patogene bakterier som kan gi alvorlig sykdom, spesielt hos barn, eldre og personer med underliggende sykdommer. Resultatene underbygger viktigheten av Ă„ pasteurisere melk for Ă„ sikre mattryggheten, og at det er avgjĂžrende Ă„ lagre rĂ„ melk ved kontinuerlig lave temperaturer for Ă„ forebygge vekst av zoonotiske patogener

    Kirchhoff-Love shell representation and analysis using triangle configuration B-splines

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    This paper presents the application of triangle configuration B-splines (TCB-splines) for representing and analyzing the Kirchhoff-Love shell in the context of isogeometric analysis (IGA). The Kirchhoff-Love shell formulation requires global C1C^1-continuous basis functions. The nonuniform rational B-spline (NURBS)-based IGA has been extensively used for developing Kirchhoff-Love shell elements. However, shells with complex geometries inevitably need multiple patches and trimming techniques, where stitching patches with high continuity is a challenge. On the other hand, due to their unstructured nature, TCB-splines can accommodate general polygonal domains, have local refinement, and are flexible to model complex geometries with C1C^1 continuity, which naturally fit into the Kirchhoff-Love shell formulation with complex geometries. Therefore, we propose to use TCB-splines as basis functions for geometric representation and solution approximation. We apply our method to both linear and nonlinear benchmark shell problems, where the accuracy and robustness are validated. The applicability of the proposed approach to shell analysis is further exemplified by performing geometrically nonlinear Kirchhoff-Love shell simulations of a pipe junction and a front bumper represented by a single patch of TCB-splines
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