Aalborg University
  • Aalborg, Denmark
Recent publications
With the advantages of fast response, good stability, and strong robustness to filter parameter variations as well as load perturbations, the application of passivity-based control (PBC) in the single-phase voltage source inverter (SPVSI) has received extensive attention. However, as a non-linear control strategy, PBC control has a complex coupling structure containing feed-forward, negative feedback, etc., which poses many difficulties for multi-damping design. In addition, ensuring the passivity of the islanded system under various load conditions or filter parameter drift is still an unresolved issue in the damping coefficient design of the PBC controller. Therefore, for SPVSI systems with LC filters, this paper proposes a multi-damping coefficient design method for PBC controllers based on stability domain analysis. The designed PBC controller can ensure system stability under several load conditions or certain deviations of LC filter parameters. Moreover, the design order from the inner to the outer control loop is adopted according to the distribution of individual damping coefficients in the control loop. As a result, each control loop has a good dynamic performance and distinct control bandwidth. Finally, robustness analysis, simulation, and experimental results verify the correctness and effectiveness of the proposed multi-damping coefficients design method for the PBC controller.
The objective of this study is to estimate the, probably correlated, ligament material properties and attachment sites in a highly non-linear, musculoskeletal knee model based on kinematic data of a knee rig experiment for seven specific specimens. Bayesian parameter estimation is used to account for uncertainty in the limited experimental data by optimization of a high dimensional input parameter space (50 parameters) consistent with all probable solutions. The set of solutions accounts for physiologically relevant ligament strain (ϵ<6%). The transitional Markov Chain Monte Carlo algorithm was used. Alterations to the algorithm were introduced in order to avoid premature convergence. To perform the parameter estimation with feasible computational cost, a surrogate model of the knee model was trained. Results show that there is a large intra- and inter-specimen variability in ligament properties, and that multiple sets of ligament properties fit the experimentally measured tibio-femoral kinematics. Although all parameters were allowed to vary significantly, large interdependence is only found between the reference strain and attachment sites. The large variation between specimens and interdependence between reference strain and attachment sites within one specimen, show the inability to identify a small range of ligament properties representative for the patient population. To limit ligament properties uncertainty in clinical applications, research will need to invest in establishing patient-specific uncertainty ranges and/or accurate in vivo measuring methods of the attachment sites and reference strain and/or alternative (combinations of) movements that would allow identifying a unique solution.
Over the past decades, significant revolutions have occurred on electricity market to reduce the electricity cost and increase profits. In particular, the novel structures facilitate the electricity manufacturers to participate in the market and earn more profit by cooperate with other producers. This paper presents a three-level gameplay-based intelligent structure to evaluate individual and collaborative strategies of electricity manufacturers, considering network and physical constraints. At the Level I, the particle swarm optimization (PSO) algorithm is implemented to determine the optimum power of distributed energy resources (DERs) in the power grid, to maximize the profits. Further, the fuzzy logic algorithm is applied to model the intermittent nature of the renewable sources and implement load demand in the power grid. At the Level II, DERs are classified into two different fuzzy logic groups to secure the fairness between every participant. Finally, at the Level III, the DERs in each group are combined each other by cooperative game theory-based algorithms to increase the coalition profits. Thereafter, Shapley, Nucleolus, and merge/split methods are applied to allocate a fair profit allocation by coalition formation. Ultimately, the results verify the proposed model influence electric players to find effective collaborative strategies under different conditions and environments.
This paper addressed the optimal operation of the microgrids (MGs) containing both the electrical and thermal loads. Combined Heat and Power (CHP) units, boilers, wind turbines, storage devices, Demand Response Resources (DRRs), as well as the power exchange possibility with the upstream wholesale market are the energy resources that have been considered as the portfolio of the decision maker. The aim of the MG operator is to provide the electrical and thermal loads of the network in the most economic and flexible way. Therefore, suitable metrics are developed in this paper in order to evaluate the flexibility of the system for both the electrical and thermal units. Then, considering both the flexibility and financial concerns, a multiobjective framework is proposed. The Information Gap Decision Theory (IGDT) method is employed to handle the uncertainties of the proposed problem. Since the objectives of the MG operator might be conflicting with each other, an efficient optimization approach should be employed to properly satisfy the operator’s policies. The Normalized Normal Constraint (NNC) approach is employed here as an efficient method that is able to find the evenly distributed Pareto solutions. The IEEE 33-bus test system is utilized to simulate the proposed problem and analyze the results. The performance of the proposed problem is approved using different scenarios. The simulation results justify the advantages and necessities of the proposed problem.
To prevent serious malfunctions and reduce the impact of faults during an emergency state of a power system, protection systems are required to have disturbance and fault state identification abilities. In this study, a novel fault diagnosis framework based on deep learning with anti-disturbance ability is proposed to identify the fault state and fault type information, even under the influence of system disturbance. The framework consists of two parts: unsupervised and supervised learning. Specifically, an unsupervised deep auto-encoder (DAE) is applied for offline feature selection and data cleaning. The DAE can extract key fault features and significantly improve the fault detection accuracy. Furthermore, two supervised convolutional neural networks are used to learn key fault feature extraction online from complex operation information in power systems and assess the fault situation and type. Using case studies, the proposed method was implemented and compared with existing intelligent methods. The results indicate that the proposed framework has a better performance in terms of fault state identification and protection malfunction prevention.
Electricity generation is one of the major concerns of today’s world due to increasing dependence on this energy. Continuity in power supply and service to network consumers may be disrupted by various disturbances; thus, the power grid requires a reliable and fast protection system. The protection of distribution networks is conventionally performed by overcurrent protective devices which preserving their coordination is a complex task due to bidirectional power flow resulting from the presence of distributed generation (DG) units. This paper presents an adaptive scheme to restore protection coordination in the distribution networks with a high penetration level of DG units. Using the multi-agent system (MAS) structure, the main protection technique monitors the current of the lateral fuse by an intelligent electronic device (IED) to estimate the operating time of the fuse and sends this data to the upstream recloser IED. Then, the recloser IED determines the recloser operating time to preserve the protection coordination. In the case of failing the main technique, the backup technique protects the distribution feeder by using a definite time piecewise linear curve for the recloser based on the proposed recloser current coefficient. The backup technique is independent of communication links. The reliable performance of the developed scheme is assessed on the ETAP simulation model of the IEEE 33 bus test system.
RNA therapeutics comprise a diverse group of oligonucleotide-based drugs such as antisense oligonucleotides (ASOs), small interfering RNAs (siRNAs), and short hairpin RNAs (shRNAs) that can be designed to selectively interact with drug targets currently undruggable with small molecule-based drugs or monoclonal antibodies. Furthermore, RNA-based therapeutics have the potential to modulate entire disease pathways, and thereby represent a new modality with unprecedented potential for generating disease-modifying drugs for a wide variety of human diseases, including central nervous system (CNS) disorders. Here, we describe different strategies for delivering RNA drugs to the CNS and review recent advances in clinical development of ASO drugs and siRNA-based therapeutics for the treatment of neurological diseases and neuromuscular disorders.Abbreviations 2'-MOE: 2'-O-(2-methoxyethyl); 2'-O-Me: 2'-O-methyl; 2'-F: 2'-fluoro; AD: Alzheimer's disease; ALS: Amyotrophic lateral sclerosis; ALSFRS-R: Revised Amyotrophic Lateral Sclerosis Functional Rating Scale; ARC: Antibody siRNA Conjugate; AS: Angelman Syndrome; ASGRP: Asialoglycoprotein receptor; ASO: Antisense oligonucleotide; AxD: Alexander Disease; BBB: Blood brain barrier; Bp: Basepair; CNM: Centronuclear myopathies; CNS: Central Nervous System; CPP: Cell-penetrating Peptide; CSF: Cerebrospinal fluid; DMD: Duchenne muscular dystrophy; DNA: Deoxyribonucleic acid; FAP: Familial amyloid polyneuropathy; FALS: Familial amyotrophic lateral sclerosis; FDA: The United States Food and Drug Administration; GalNAc: N-acetylgalactosamine; GoF: Gain of function; hATTR: Hereditary transthyretin amyloidosis; HD: Huntington's disease; HRQOL: health-related quality of life; ICV: Intracerebroventricular; IT: Intrathecal; LNA: Locked nucleic acid; LoF: Loss of function; mRNA: Messenger RNA; MS: Multiple Sclerosis; MSA: Multiple System Atrophy; NBE: New Biological Entity; NCE: New Chemical Entity; NHP: Nonhuman primate; nt: Nucleotide; PD: Parkinson's disease; PNP: Polyneuropathy; PNS: Peripheral nervous system; PS: Phosphorothioate; RISC: RNA-Induced Silencing Complex; RNA: Ribonucleic acid; RNAi: RNA interference; s.c.: Subcutaneous; siRNA: Small interfering RNA; SMA: Spinal muscular atrophy; SMN: Survival motor neuron; TTR: Transthyretin.
Erdheim-Chester disease (ECD) is a rare non-Langerhans cell histiocytosis. Erdheim-Chester disease is considered a potentially severe multisystemic disease with life-threatening manifestations due to the compression of normal structures. Recently, mutation of the proto-oncogene BRAF (BRAFV600E) has been found in 100% of cases. The common sites of involvement are the skeleton, central nervous system, cardiovascular system, lungs, retroperitoneum, and skin. We present the autopsy case of a 48-year-old White woman with an unknown tumor of the heart, where finally autopsy revealed the diagnosis of ECD. The clinical, radiological, and pathological manifestations associated with ECD are highlighted.
Microbial communities in activated sludge (AS) are the core of sanitation in wastewater treatment plants (WWTPs). Microbial communities in AS have shown seasonal changes, however, long-term experiments (>2 years) are rarely conducted, limiting our understanding of the true seasonal dynamics in WWTPs. In this study, we resolved the microbial seasonal dynamics at the species level in four municipal full-scale WWTPs, sampled every 7–10 days, during 3–5 consecutive years. By applying a new time-series analysis approach, we revealed that the seasonal pattern was species-specific, where species belonging to the same functional guild or genus may show different seasonal dynamics. Species could be grouped into cohorts according to their seasonal patterns, where seasonal cohorts showed repeatable annual dynamics across years and plants. Species were also grouped according to their net growth rate in the AS (i.e., growing species and disappearing species). Growing species were more prevailing in spring and autumn cohorts, while disappearing species, which were only present due to the continuous immigration from influent wastewater, were mostly associated with winter and spring cohorts. Most known process-critical species, such as nitrifiers, polyphosphate accumulating organisms and filamentous organisms, showed distinct species-specific patterns. Overall, our study showed that overarching seasonal patterns affected microbial species in full-scale AS plants, with similar seasonal patterns across plants for many dominant species. These recurrent seasonal variations should be taken into account in the operation, understanding and management of the WWTPs.
Background International guidelines do not recommend routine imaging, including magnetic resonance imaging (MRI), and seek to guide clinicians only to refer for imaging based on specific indications. Despite this, several studies show an increase in the use of MRI among patients with low back pain (LBP) and an imbalance between appropriate versus inappropriate use of MRI for LBP. This study aimed to investigate to what extent referrals from general practice for lumbar MRI complied with clinical guideline recommendations in a Danish setting, contributing to the understanding and approaches to lumbar MRI for all clinicians managing LBP in the primary sector. Materials and methods From 2014 to 2018, all referrals for lumbar MRI were included from general practitioners in the Central Denmark Region for diagnostic imaging at a public regional hospital. A modified version of the American College of Radiology Imaging Appropriateness Criteria for LBP was used to classify referrals as appropriate or inappropriate, based on the unstructured text in the GPs’ referrals. Appropriate referrals included fractures, cancer, symptoms persisting for more than 6 weeks of non-surgical treatment, previous surgery, candidate for surgery or suspicion of cauda equina. Inappropriate referrals were sub-classified as lacking information about previous non-surgical treatment and duration. Results Of the 3772 retrieved referrals for MRI of the lumbar spine, 55% were selected and a total of 2051 referrals were categorised. Approximately one quarter (24.5%) were categorised as appropriate, and 75.5% were deemed inappropriate. 51% of the inappropriate referrals lacked information about previous non-surgical treatment, and 49% had no information about the duration of non-surgical treatment. Apart from minor yearly fluctuations, there was no change in the distribution of appropriate and inappropriate MRI referrals from 2014 to 2018. Conclusion The majority of lumbar MRI referrals (75.5%) from general practitioners for lumbar MRI did not fulfil the ACR Imaging Appropriateness Criteria for LBP based on the unstructured text of their referrals. There is a need for referrers to include all guideline-relevant information in referrals for imaging. More research is needed to determine whether this is due to patients not fulfilling guideline recommendations or simply the content of the referrals.
Background Introducing interprofessional education (IPE) in healthcare curricula can prepare students for healthcare practices that have become increasingly complex. The use of simulation is promoted to support IPE. This study explores healthcare students’ experiences of participating in common, sub-acute patient scenarios that routinely occur in clinical practice in primary care. More specifically, it looks at how sub-acute patient scenarios from primary care can help develop interprofessional collaborative competence. Methods Medical students ( N = 10), master’s students in advanced geriatric nursing ( N = 8) and bachelor’s students in nursing ( N = 9) participated in the simulations. The students were in their last or second-to-last year of education. We conducted five semi-structured focus group interviews with the participants’ directly after the simulation training to elicit experiences related to the scenarios, the simulation and interprofessional collaboration. The transcripts were analysed using systematic text condensation. To supplement the focus group interviews, the students also completed the interprofessional collaborative competency attainment survey (ICCAS), which measures the students’ self-assessed interprofessional competence. Results Three main themes emerged from the analysis of the focus group interviews: realism , uncertainty and reflection . The students emphasised the importance of authentic and recognisable scenarios. They said the vague and unspecific patient symptoms created uncertainty in the situation, making it difficult to understand the patient’s diagnosis. Despite that uncertainty, they described the experience as positive. Further, the students expressed that the simulation increased their confidence in interprofessional collaboration and prepared them for future work. The results from the ICCAS questionnaire showed that the students reported a subjective positive change in their interprofessional competence after participating in the scenarios. Conclusions This study showed that simulation-based IPE with sub-acute primary care scenarios contributes to develop interprofessional collaborative competence in healthcare education. Sub-acute scenarios can supplement the more common approaches with acute care scenarios and aid in developing the collaborative competence required to work in healthcare teams.
Background Vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are used to reduce the risk of developing Coronavirus Disease 2019 (COVID-19). Despite the significant benefits in terms of reduced risk of hospitalization and death, different adverse events may present after vaccination: among them, headache is one of the most common, but nowadays there is no summary presentation of its incidence and no description of its main features. Methods We searched PubMed and EMBASE covering the period between January 1 st 2020 and August 6 th , 2021, looking for record in English and with an abstract and using three main search terms (with specific variations): COVID-19/SARS-CoV-2; Vaccination; headache/adverse events. We selected manuscript including information on subjects developing headache after injection, and such information had to be derived from a structured form (i.e. no free reporting). Pooled estimates and 95% confidence intervals were calculated. Analyses were carried out by vaccine vs. placebo, by first vs. second dose, and by mRNA-based vs. “traditional” vaccines; finally, we addressed the impact of age and gender on post-vaccine headache onset. Results Out of 9338 records, 84 papers were included in the review, accounting for 1.57 million participants, 94% of whom received BNT162b2 or ChAdOx1. Headache was generally the third most common AE: it was detected in 22% (95% CI 18–27%) of subjects after the first dose of vaccine and in 29% (95% CI 23–35%) after the second, with an extreme heterogeneity. Those receiving placebo reported headache in 10–12% of cases. No differences were detected across different vaccines or by mRNA-based vs. “traditional” ones. None of the studies reported information on headache features. A lower prevalence of headache after the first injection of BNT162b2 among older participants was shown. Conclusions Our results show that vaccines are associated to a two-fold risk of developing headache within 7 days from injection, and the lack of difference between vaccine types enable to hypothesize that headache is secondary to systemic immunological reaction than to a vaccine-type specific reaction. Some descriptions report onset within the first 24 h and that in around one-third of the cases, headache has migraine-like features with pulsating quality, phono and photophobia; in 40–60% of the cases aggravation with activity is observed. The majority of patients used some medication to treat headache, the one perceived as the most effective being acetylsalicylic acid.
Background In early 2021, the SARS-CoV-2 lineage B.1.1.7 (Alpha variant) became dominant across large parts of the world. In Denmark, comprehensive and real-time test, contact-tracing, and sequencing efforts were applied to sustain epidemic control. Here, we use these data to investigate the transmissibility, introduction, and onward transmission of B.1.1.7 in Denmark. Methods We analyzed a comprehensive set of 60,178 SARS-CoV-2 genomes generated from high-throughput sequencing by the Danish COVID-19 Genome Consortium, representing 34% of all positive cases in the period 14 November 2020 to 7 February 2021. We calculated the transmissibility of B.1.1.7 relative to other lineages using Poisson regression. Including all 1976 high-quality B.1.1.7 genomes collected in the study period, we constructed a time-scaled phylogeny, which was coupled with detailed travel history and register data to outline the introduction and onward transmission of B.1.1.7 in Denmark. Results In a period with unchanged restrictions, we estimated an increased B.1.1.7 transmissibility of 58% (95% CI: [56%, 60%]) relative to other lineages. Epidemiological and phylogenetic analyses revealed that 37% of B.1.1.7 cases were related to the initial introduction in November 2020. The relative number of cases directly linked to introductions varied between 10 and 50% throughout the study period. Conclusions Our findings corroborate early estimates of increased transmissibility of B.1.1.7. Both substantial early expansion when B.1.1.7 was still unmonitored and continuous foreign introductions contributed considerably to case numbers. Finally, our study highlights the benefit of balanced travel restrictions and self-isolation procedures coupled with comprehensive surveillance efforts, to sustain epidemic control in the face of emerging variants.
Switched-capacitor (SC) multilevel inverters (MLIs) offer various advantages over conventional series MLIs owing to the operation from a single DC source, self-balanced capacitor voltages, and inherent voltage gain. However, these advantages come at the cost of high current stress on the capacitors, associated semiconductors, and the DC source. This inrush current has limited the practical application of SC-MLIs to a fractional kilowatt rating. This paper proposes a practical solution by introducing a unity gain 9-level active-neutral-point-clamped SC-MLI with an alleviated capacitor charging current. Switching loss in the SC circuit semiconductors is reduced by using a modified switching sequence that allows the charging current to flow with a minimum interruption till it is turned off at zero-current crossing. The structure also integrates protection against the detrimental effects of voltage transients across the charging inductor. The inverter exhibits a maximum efficiency of 98.03% at 583.91 W output power. Compared with similar SC-MLIs with hard-charging, the proposed inverter significantly reduces the losses and improves efficiency. Experimental results from a 2 kVA laboratory prototype validate the theoretical analysis and show the capacitor voltages stable under varying load impedance and modulation index.
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12,594 members
Pooya Davari
  • Department of Energy Technology
Meg Duroux
  • Department of Health Science and Technology
Hiva Alipour
  • Department of Health Science and Technology
Cristiano Varrone
  • Department of Chemistry and Bioscience
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Per Michael Johansen
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