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| 3. |
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| 4. |
- Jakosky, B. M., et al.
(författare)
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MAVEN observations of the response of Mars to an interplanetary coronal mass ejection
- 2015
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 350:6261
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Tidskriftsartikel (refereegranskat)abstract
- Coupling between the lower and upper atmosphere, combined with loss of gas from the upper atmosphere to space, likely contributed to the thin, cold, dry atmosphere of modern Mars. To help understand ongoing ion loss to space, the Mars Atmosphere and Volatile Evolution (MAVEN) spacecraft made comprehensive measurements of the Mars upper atmosphere, ionosphere, and interactions with the Sun and solar wind during an interplanetary coronal mass ejection impact in March 2015. Responses include changes in the bow shock and magnetosheath, formation of widespread diffuse aurora, and enhancement of pick-up ions. Observations and models both show an enhancement in escape rate of ions to space during the event. Ion loss during solar events early in Mars history may have been a major contributor to the long-term evolution of the Mars atmosphere.
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| 5. |
- Sheykhifard, Z., et al.
(författare)
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Magnetic graphene/Ni-nano-crystal hybrid for small field magnetoresistive effect synthesized via electrochemical exfoliation/deposition technique
- 2018
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Ingår i: Journal of Materials Science-Materials in Electronics. - : Springer Science and Business Media LLC. - 0957-4522 .- 1573-482X. ; 29:5, s. 4171-4178
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Tidskriftsartikel (refereegranskat)abstract
- Two-dimensional heterostructures of graphene (Gr) and metal/semiconducting elements convey new direction in electronic devices. They can be useful for spintronics because of small spin orbit interaction of Gr as a non-magnetic metal host with promising electrochemical stability. In this paper, we demonstrate one-step fabrication of magnetic Ni-particles entrapped within Gr-flakes based on simultaneous electrochemical exfoliation/deposition procedure by two-electrode system using platinum as the cathode electrode and a graphite foil as the anode electrode. The final product is an air stable hybrid element including Gr flakes hosting magnetic Ni-nano-crystals showing superparamagnetic-like response and room temperature giant magnetoresistance (GMR) effect at small magnetic field range. The GMR effect is originated from spin scattering through ferromagnetic/non-magnetic nature of Ni/Gr heterostructure and interpreted based on a phenomenological spin transport model. Our work benefits from XRD, XPS, Raman, TEM, FTIR and VSM measurements We addressed that how our results can be used for rapid manufacturing of magnetic Gr for low field magneto resistive elements and potential printed spintronic devices.
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| 6. |
- Saheli, M, et al.
(författare)
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Athletes' Mesenchymal Stem Cells Could Be the Best Choice for Cell Therapy in Omicron-Infected Patients
- 2022
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Ingår i: Cells. - : MDPI AG. - 2073-4409. ; 11:12
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Tidskriftsartikel (refereegranskat)abstract
- New severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, Omicron, contains 32 mutations that have caused a high incidence of breakthrough infections or re-infections. These mutations have reduced vaccine protection against Omicron and other new emerging variants. This highlights the need to find effective treatment, which is suggested to be stem cell-based therapy. Stem cells could support respiratory epithelial cells and they could restore alveolar bioenergetics. In addition, they can increase the secretion of immunomodulatory cytokines. However, after transplantation, cell survival and growth rate are low because of an inappropriate microenvironment, and stem cells face ischemia, inflammation, and oxidative stress in the transplantation niche which reduces the cells’ survival and growth. Exercise-training can upregulate antioxidant, anti-inflammatory, and anti-apoptotic defense mechanisms and increase growth signaling, thereby improving transplanted cells’ survival and growth. Hence, using athletes’ stem cells may increase stem-cell therapy outcomes in Omicron-affected patients.
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| 7. |
- Haghighi, Mona, et al.
(författare)
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A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study
- 2016
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- Regression models are extensively used in many epidemiological studies to understand the linkage between specific outcomes of interest and their risk factors. However, regression models in general examine the average effects of the risk factors and ignore subgroups with different risk profiles. As a result, interventions are often geared towards the average member of the population, without consideration of the special health needs of different subgroups within the population. This paper demonstrates the value of using rule-based analysis methods that can identify subgroups with heterogeneous risk profiles in a population without imposing assumptions on the subgroups or method. The rules define the risk pattern of subsets of individuals by not only considering the interactions between the risk factors but also their ranges. We compared the rule-based analysis results with the results from a logistic regression model in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Both methods detected a similar suite of risk factors, but the rule-based analysis was superior at detecting multiple interactions between the risk factors that characterize the subgroups. A further investigation of the particular characteristics of each subgroup may detect the special health needs of the subgroup and lead to tailored interventions.
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| 8. |
- Rahmati, E., et al.
(författare)
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Thermoelectric properties of doped graphene nanoribbons: density functional theory calculations and electrical transport
- 2022
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Ingår i: RSC Advances. - : ROYAL SOC CHEMISTRY. - 2046-2069. ; 12:10, s. 6174-6180
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Tidskriftsartikel (refereegranskat)abstract
- We present a detailed study on band structure-dependent properties such as electrical conductivity, the charge of carriers and Seebeck coefficients of graphene nano-ribbons (GNRs) doped with the magnetic impurities Fe and Co since the spin thermopower could be considerably enhanced by impurities. Thermoelectric properties of two-dimensional systems are currently of great interest due to the possibility of heat to electrical energy conversion at the nanoscale. The thermoelectric properties are investigated using the semi-classical Boltzmann method. The electronic band structure of doped nano-ribbons is evaluated by means of density-functional theory in which the Hubbard interaction is considered. Different types of nano-ribbons (armchair-edge and zigzag-edge) and their thermoelectric features such as conductivity and Seebeck coefficient in the presence and absence of magnetic impurities have been studied.
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| 9. |
- Darabi, H., et al.
(författare)
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Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood
- 2021
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Ingår i: Geocarto International. - : Taylor and Francis Ltd.. - 1010-6049 .- 1752-0762.
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Tidskriftsartikel (refereegranskat)abstract
- In this study, a new hybridized machine learning algorithm for urban flood susceptibility mapping, named MultiB-MLPNN, was developed using a multi-boosting technique and MLPNN. The model was tested in Amol City, Iran, a data-scarce city in an ungauged area which is prone to severe flood inundation events and currently lacks flood prevention infrastructure. Performance of the hybridized model was compared with that of a standalone MLPNN model, random forest and boosted regression trees. Area under the curve, efficiency, true skill statistic, Matthews correlation coefficient, misclassification rate, sensitivity and specificity were used to evaluate model performance. In validation, the MultiB-MLPNN model showed the best predictive performance. The hybridized MultiB-MLPNN model is thus useful for generating realistic flood susceptibility maps for data-scarce urban areas. The maps can be used to develop risk-reduction measures to protect urban areas from devastating floods, particularly where available data are insufficient to support physically based hydrological or hydraulic models.
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| 10. |
- Johnson, Randi K., et al.
(författare)
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Metabolite-related dietary patterns and the development of islet autoimmunity
- 2019
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- The role of diet in type 1 diabetes development is poorly understood. Metabolites, which reflect dietary response, may help elucidate this role. We explored metabolomics and lipidomics differences between 352 cases of islet autoimmunity (IA) and controls in the TEDDY (The Environmental Determinants of Diabetes in the Young) study. We created dietary patterns reflecting pre-IA metabolite differences between groups and examined their association with IA. Secondary outcomes included IA cases positive for multiple autoantibodies (mAb+). The association of 853 plasma metabolites with outcomes was tested at seroconversion to IA, just prior to seroconversion, and during infancy. Key compounds in enriched metabolite sets were used to create dietary patterns reflecting metabolite composition, which were then tested for association with outcomes in the nested case-control subset and the full TEDDY cohort. Unsaturated phosphatidylcholines, sphingomyelins, phosphatidylethanolamines, glucosylceramides, and phospholipid ethers in infancy were inversely associated with mAb+ risk, while dicarboxylic acids were associated with an increased risk. An infancy dietary pattern representing higher levels of unsaturated phosphatidylcholines and phospholipid ethers, and lower sphingomyelins was protective for mAb+ in the nested case-control study only. Characterization of this high-risk infant metabolomics profile may help shape the future of early diagnosis or prevention efforts. © 2019, The Author(s).
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| 11. |
- Ketzer, João Marcelo, et al.
(författare)
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Near seafloor methane flux in the world's largest human-induced dead zone is regulated by sediment accumulation rate
- 2024
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Ingår i: Marine Geology. - : Elsevier. - 0025-3227 .- 1872-6151. ; 468
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Tidskriftsartikel (refereegranskat)abstract
- The vast oxygen-depleted area of the central Baltic Sea is the largest human-induced dead zone in the world with 70,000 km(2) or approximately three times the second largest one in the Gulf of Mexico. Methane occurs in high concentrations in bottom waters (3200 nM) and sediments (30 mM), and its dynamics is better constrained for the water column, but still poorly understood on sediments. Here we show that sediment accumulation rate plays a major role in regulating the quantity of organic matter and its residence time in the sulphate reduction and methanogenesis zones and, therefore, affects methane generation, consumption, and diffusive flux in sediments near the seafloor (< 1 m). High fluxes found in high sediment accumulation rate areas and competition for substrate (organoclastic sulphate reduction vs. anaerobic oxidation of methane with sulphate), compromise the ability of the thin microbial filter to consume and prevent methane diffusion through the seafloor.
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| 12. |
- Lundgren, Markus, et al.
(författare)
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Analgesic antipyretic use among young children in the TEDDY study : No association with islet autoimmunity
- 2017
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Ingår i: BMC Pediatrics. - : Springer Science and Business Media LLC. - 1471-2431. ; 17:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: The use of analgesic antipyretics (ANAP) in children have long been a matter of controversy. Data on their practical use on an individual level has, however, been scarce. There are indications of possible effects on glucose homeostasis and immune function related to the use of ANAP. The aim of this study was to analyze patterns of analgesic antipyretic use across the clinical centers of The Environmental Determinants of Diabetes in the Young (TEDDY) prospective cohort study and test if ANAP use was a risk factor for islet autoimmunity. Methods: Data were collected for 8542 children in the first 2.5 years of life. Incidence was analyzed using logistic regression with country and first child status as independent variables. Holm's procedure was used to adjust for multiplicity of intercountry comparisons. Time to autoantibody seroconversion was analyzed using a Cox proportional hazards model with cumulative analgesic use as primary time dependent covariate of interest. For each categorization, a generalized estimating equation (GEE) approach was used. Results: Higher prevalence of ANAP use was found in the U.S. (95.7%) and Sweden (94.8%) compared to Finland (78.1%) and Germany (80.2%). First-born children were more commonly given acetaminophen (OR 1.26; 95% CI 1.07, 1.49; p = 0.007) but less commonly Non-Steroidal Anti-inflammatory Drugs (NSAID) (OR 0.86; 95% CI 0.78, 0.95; p = 0.002). Acetaminophen and NSAID use in the absence of fever and infection was more prevalent in the U.S. (40.4%; 26.3% of doses) compared to Sweden, Finland and Germany (p < 0.001). Acetaminophen or NSAID use before age 2.5 years did not predict development of islet autoimmunity by age 6 years (HR 1.02, 95% CI 0.99-1.09; p = 0.27). In a sub-analysis, acetaminophen use in children with fever weakly predicted development of islet autoimmunity by age 3 years (HR 1.05; 95% CI 1.01-1.09; p = 0.024). Conclusions: ANAP use in young children is not a risk factor for seroconversion by age 6 years. Use of ANAP is widespread in young children, and significantly higher in the U.S. compared to other study sites, where use is common also in absence of fever and infection.
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| 13. |
- Madanian, H., et al.
(författare)
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Suprathermal electrons near the nucleus of comet 67P/Churyumov-Gerasimenko at 3AU : Model comparisons with Rosetta data
- 2016
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Ingår i: Journal of Geophysical Research - Space Physics. - 2169-9380 .- 2169-9402. ; 121:6, s. 5815-5836
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Tidskriftsartikel (refereegranskat)abstract
- Observations of the coma near the nucleus of comet 67P/Churyumov-Gerasimenko (67P) made by the IES (Ion and Electron Sensor) instrument onboard the Rosetta Orbiter during late 2014 showed that electron fluxes greatly exceeded solar wind electron fluxes. The IES is part of the Rosetta Plasma Consortium. This paper reports on electron energy spectra measured by IES near the nucleus as well as approximate densities and average energies for the suprathermal electrons when the comet was at a heliocentric distance of about 3 AU. Comparisons are made with electron densities measured by other instruments. The high electron densities observed (e.g., n(e) approximate to 10-100 cm(-3)) must be associated with the cometary ion density enhancement created mainly by the photoionization of cometary gas by solar radiation; there are other processes that also contribute. Quasineutrality requires that the electron and ion densities be the same, and under certain conditions an ambipolar electric field is required to achieve quasi-neutrality. We present the results of a test particle model of cometary ion pickup by the solar wind and a two-stream electron transport code and use these results to interpret the IES data. We also estimate the effects on the electron spectrum of a compression of the electron fluid parcel. The electrons detected by IES can have energies as high as about 100-200 eV near the comet on some occasions, in which case the hot electrons can significantly enhance ionization rates of neutrals via impact ionization.
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| 14. |
- Moradi, Fereidoun, et al.
(författare)
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Monitoring Cyber-Physical Systems Using a Tiny Twin to Prevent Cyber-Attacks
- 2022
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Ingår i: Lecture Notes in Computer Science, vol. 13255. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031150760 ; , s. 24-43
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Konferensbidrag (refereegranskat)abstract
- We propose a method to detect attacks on sensors and controllers in cyber-physical systems. We develop a monitor that uses an abstract digital twin, Tiny Twin, to detect false sensor data and faulty control commands. The Tiny Twin is a state transition system that represents the observable behavior of the system from the monitor point of view. At runtime, the monitor observes the sensor data and the control commands, and checks whether the observed data and commands are consistent with the state transitions in the Tiny Twin. The monitor produces an alarm when an inconsistency is detected. We model the components of the system and the physical processes in the Rebeca modeling language and use its model checker to generate the state space. The Tiny Twin is built automatically by reducing the state space, keeping the observable behavior of the system, and preserving the trace equivalence. We demonstrate the method and evaluate it in detecting attacks using a temperature control system.
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| 15. |
- Panahi, Mahdi, et al.
(författare)
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Large-scale dynamic flood monitoring in an arid-zone floodplain using SAR data and hybrid machine-learning models
- 2022
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Ingår i: Journal of Hydrology. - : Elsevier BV. - 0022-1694 .- 1879-2707. ; 611, s. 128001-
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Tidskriftsartikel (refereegranskat)abstract
- Although the growing number of synthetic aperture radar (SAR) satellites has increased their application in flood-extent mapping, predictive models for the analysis of flood dynamics that are independent of sensor characteristics must be developed to fully extract information from SAR images for flood mitigation. This study aimed to develop hybrid machine-learning models for flood mapping in the Ahvaz region, Iran, based on SAR data. Each hybrid model consists of a support vector machine (SVM) algorithm coupled with one of the following metaheuristic optimization procedures: grey wolf optimization (GWO), differential evolution, and the imperialist competitive algorithm. Sentinel-1 acquired SAR images before and during flooding between 20 March and 26 May of 2019. The goodness-of-fit level and predictive capability of each model were scrutinized using overall accuracy, producer accuracy, and user accuracy. The SVM-GWO approach yielded the highest accuracy with overall accuracies of 96.07% and 93.39% in the training and validation steps, respectively. Furthermore, this hybrid model provided the most accurate classification of water-inundation class based on producer accuracy (96.67%) and user accuracy (95.05%). The results highlight that wetland is the last land-use/land-cover type to return to normal conditions due to the many previously dry oxbow lakes that could trap water for a long time. Furthermore, the nine most suitable sites for flood-protection structures (e.g., embankments and levees) were identified based on floodwater distribution analysis. This work describes a robust, data-parsimonious approach that will benefit flood mitigation studies seeking to identify the most suitable locations for embankments based on spatio-temporal flood dynamics.
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| 16. |
- Rahmati, O., et al.
(författare)
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Development of novel hybridized models for urban flood susceptibility mapping
- 2020
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Ingår i: Scientific Reports. - : Nature Research. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency of the novel hybridized and standalone SVR models for spatial modeling of urban flood inundation was evaluated using different cutoff-dependent and cutoff-independent evaluation criteria, including area under the receiver operating characteristic curve (AUC), Accuracy (A), Matthews Correlation Coefficient (MCC), Misclassification Rate (MR), and F-score. The results demonstrated that both hybridized models had very high performance (Wavelet-SVR-GWO: AUC = 0.981, A = 0.92, MCC = 0.86, MR = 0.07; Wavelet-SVR-Bat: AUC = 0.972, A = 0.88, MCC = 0.76, MR = 0.11) compared with the standalone SVR (AUC = 0.917, A = 0.85, MCC = 0.7, MR = 0.15). Therefore, these hybridized models are a promising, cost-effective method for spatial modeling of urban flood susceptibility and for providing in-depth insights to guide flood preparedness and emergency response services.
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| 17. |
- Rezaie, Fatemeh, et al.
(författare)
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Improving landslide susceptibility mapping using integration of ResU-Net technique and optimized machine learning algorithms
- 2023
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Ingår i: Remote Sensing of Soil and Land Surface Processes: Monitoring, Mapping, and Modeling. - : Elsevier BV. ; , s. 419-438
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Landslides are the most common natural disasters in mountainous areas that follow major seismic events, volcanic activity, melting snow, or prolonged and intense rainfalls and cause severe disruptions to ecosystems, economies, and societies worldwide. Therefore, minimizing their negative effects through landslide-susceptibility assessment is essential. In this study, the standard support vector regression (SVR) integrated with the gray wolf optimizer (GWO) and particle swarm optimization (PSO) algorithms were used to map landslide-prone areas. The landslide inventory map was automatically generated using a pixel-based technique based on residual U-Net algorithm from the Sentinel-2 data. In total, 4900 landslide samples were identified and divided randomly into two groups, creating training (70%) and testing (30%) datasets. In addition, nine factors that affect landslides were selected to construct a model using each algorithm. Finally, the performance of the models (SVR, SVR-GWO, and SVR-PSO) were validated and compared using the area under the receiver operating characteristic curve. The findings showed that the hybrid SVR-GWO model performed better than the standard model and is recommended for landslide susceptibility assessment due to its accuracy and efficiency.
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| 18. |
- Sanaeinasab, H., et al.
(författare)
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Development and Psychometric Assessment of the COVID-19 Health Literacy Scale : Preliminary Testing and Factor Structure
- 2022
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Ingår i: Journal of Health Literacy. - : Mashhad University of Medical Sciences. - 2476-4728. ; 6:4, s. 32-46
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Tidskriftsartikel (refereegranskat)abstract
- Background and Objective: Improving the health literacy in the different populations regarding COVID-19 may be useful in the control of its prevalence. This study examined the psychometric properties of a newly developed disease-specific measure of health literacy related to COVID-19 to be used as a standard measure. Materials and Methods: Relevant literature was reviewed to identify an item pool, and an expert panel was convened to choose items that might be included in the scale. Content validity ratio (CVR) and content validity index (CVI) was determined and face validity was examined by calculating the impact score in a group of social media users. The factor structure of the initial scale was examined in 590 Iranian individuals participating in online social networks in September 2020. Internal consistency of the scale was assessed by Cronbach’s alpha and test-retest reliability of responses was measured by Pearson correlation coefficients. Results: A five-factor solution for the 51-items scale was obtained through exploratory factor analysis. The five main dimensions were understanding, communication, information seeking, analysis, and behavior. The dimensions explained 47% of the variance in scale scores. Participants whose scores fell in the high category (27%) were significantly different compared to those whose scores fell in the low category (27%) on all dimensions (p<0.001). The CVR values for all items were greater than 0.85 and all items also got CVI values higher than 0.79 based on nine-person expert panel. The Cronbach’s alpha for the overall scale was 0.89, and it was ranged from 0.71 to 0.90. Test-retest reliability for the scale was high (r=0.89). Conclusion: Health Literacy Scale for protect against COVID-19is a valid and reliable measure for Iranian population. This measure should be translated, and administered, in other settings to replicate the results obtained here.
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| 19. |
- Shahabi, Himan, et al.
(författare)
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Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach : Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier
- 2020
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Ingår i: Remote Sensing. - Switzerland : MDPI. - 2072-4292. ; 12:2, s. 1-30
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Tidskriftsartikel (refereegranskat)abstract
- Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.
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