| 1. |
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| 2. |
- Boobalan, Parimala, et al.
(författare)
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Fusion of Federated Learning and Industrial Internet of Things : A survey
- 2022
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Ingår i: Computer Networks. - : Elsevier. - 1389-1286 .- 1872-7069. ; 212
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Tidskriftsartikel (refereegranskat)abstract
- Industrial Internet of Things (IIoT) lays a new paradigm for the concept of Industry 4.0 and paves an insight for new industrial era. Nowadays smart machines and smart factories use machine learning/deep learning based models for incurring intelligence. However, storing and communicating the data to the cloud and end device leads to issues in preserving privacy. In order to address this issue, Federated Learning (FL) technology is implemented in IIoT by the researchers nowadays to provide safe, accurate, robust and unbiased models. Integrating FL in IIoT ensures that no local sensitive data is exchanged, as the distribution of learning models over the edge devices has become more common with FL. Therefore, only the encrypted notifications and parameters are communicated to the central server. In this paper, we provide a thorough overview on integrating FL with IIoT in terms of privacy, resource and data management. The survey starts by articulating IIoT characteristics and fundamentals of distributed machine learning and FL. The motivation behind integrating IIoT and FL for achieving data privacy preservation and on-device learning are summarized. Then we discuss the potential of using machine learning (ML), deep learning (DL) and blockchain techniques for FL in secure IIoT. Further we analyze and summarize several ways to handle the heterogeneous and huge data. Comprehensive background on data and resource management are then presented, followed by applications of IIoT with FL in automotive, robotics, agriculture, energy, and healthcare industries. Finally, we shed light on challenges, some possible solutions and potential directions for future research.
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| 3. |
- George, Gavin, et al.
(författare)
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South African University Staff and Students’ Perspectives, Preferences, and Drivers of Hesitancy Regarding COVID-19 Vaccines : A Multi-Methods Study
- 2022
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Ingår i: Vaccines. - : MDPI AG. - 2076-393X. ; 10:8
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Tidskriftsartikel (refereegranskat)abstract
- COVID-19 vaccine hesitancy poses a threat to the success of vaccination programmes currently being implemented. Concerns regarding vaccine effectiveness and vaccine-related adverse events are potential barriers to vaccination; however, it remains unclear whether tailored messaging and vaccination programmes can influence uptake. Understanding the preferences of key groups, including students, could guide the implementation of youth-targeted COVID-19 vaccination programmes, ensuring optimal uptake. This study examined university staff and students’ perspectives, preferences, and drivers of hesitancy regarding COVID-19 vaccines. A multi-methods approach was used—an online convenience sample survey and discrete choice experiment (DCE)—targeting staff and students at the University of KwaZulu-Natal, South Africa. The survey and DCE were available for staff and students, and data were collected from 18 November to 24 December 2021. The survey captured demographic characteristics as well as attitudes and perspectives of COVID-19 and available vaccines using modified Likert rating questions adapted from previously used tools. The DCE was embedded within the survey tool and varied critical COVID-19 vaccine programme characteristics to calculate relative utilities (preferences) and determine trade-offs. A total of 1836 staff and students participated in the study (541 staff, 1262 students, 33 undisclosed). A total of 1145 (62%) respondents reported that they had been vaccinated against COVID-19. Vaccination against COVID-19 was less prevalent among students compared with staff (79% of staff vs. 57% of students). The vaccine’s effectiveness (22%), and its safety (21%), ranked as the two dominant reasons for not getting vaccinated. These concerns were also evident from the DCE, with staff and students being significantly influenced by vaccine effectiveness, with participants preferring highly effective vaccines (90% effective) as compared with those listed as being 70% or 50% effective (β = −3.72, 95% CI = −4.39 to −3.04); this characteristic had the strongest effect on preferences of any attribute. The frequency of vaccination doses was also found to have a significant effect on preferences with participants deriving less utility from choice alternatives requiring two initial vaccine doses compared with one dose (β = −1.00, 95% CI = −1.42 to −0.58) or annual boosters compared with none (β = −2.35, 95% CI = −2.85 to −1.86). Notably, an incentive of ZAR 350 (USD 23.28) did have a positive utility (β = 1.14, 95% CI = 0.76 to 1.53) as compared with no incentive. Given the slow take-up of vaccination among youth in South Africa, this study offers valuable insights into the factors that drive hesitancy among this population. Concerns have been raised around the safety and effectiveness of vaccines, although there remains a predilection for efficient services. Respondents were not enthusiastic about the prospect of having to take boosters, and this has played out in the roll-out data. Financial incentives may increase both the uptake of the initial dose of vaccines and see a more favourable response to subsequent boosters. Universities should consider tailored messaging regarding vaccine effectiveness and facilitate access to vaccines, to align services with the stated preferences of staff and students.
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| 4. |
- Hentati-Sundberg, Jonas, et al.
(författare)
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Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research
- 2023
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Ingår i: Remote Sensing in Ecology and Conservation. - : Wiley. - 2056-3485. ; 9:4, s. 568-581
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Tidskriftsartikel (refereegranskat)abstract
- Ecological research and monitoring need to be able to rapidly convey information that can form the basis of scientifically sound management. Automated sensor systems, especially if combined with artificial intelligence, can contribute to such rapid high-resolution data retrieval. Here, we explore the prospects of automated methods to generate insights for seabirds, which are often monitored for their high conservation value and for being sentinels for marine ecosystem changes. We have developed a system of video surveillance combined with automated image processing, which we apply to common murres Uria aalge. The system uses a deep learning algorithm for object detection (YOLOv5) that has been trained on annotated images of adult birds, chicks and eggs, and outputs time, location, size and confidence level of all detections, frame-by-frame, in the supplied video material. A total of 144 million bird detections were generated from a breeding cliff over three complete breeding seasons (2019–2021). We demonstrate how object detection can be used to accurately monitor breeding phenology and chick growth. Our automated monitoring approach can also identify and quantify rare events that are easily missed in traditional monitoring, such as disturbances from predators. Further, combining automated video analysis with continuous measurements from a temperature logger allows us to study impacts of heat waves on nest attendance in high detail. Our automated system thus produces comparable, and in several cases significantly more detailed, data than those generated from observational field studies. By running in real time on the camera streams, it has the potential to supply researchers and managers with high-resolution up-to-date information on seabird population status. We describe how the system can be modified to fit various types of ecological research and monitoring goals and thereby provide up-to-date support for conservation and ecosystem management.
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| 5. |
- Javed, Abdul Rehman, et al.
(författare)
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A Survey of Explainable Artificial Intelligence for Smart Cities
- 2023
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Ingår i: Electronics. - : MDPI. - 2079-9292. ; 12:4
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Tidskriftsartikel (refereegranskat)abstract
- The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives of humans and envisioned the concept of smart cities using informed actions, enhanced user interpretations and explanations, and firm decision-making processes. The XAI systems can unbox the potential of black-box AI models and describe them explicitly. The study comprehensively surveys the current and future developments in XAI technologies for smart cities. It also highlights the societal, industrial, and technological trends that initiate the drive towards XAI for smart cities. It presents the key to enabling XAI technologies for smart cities in detail. The paper also discusses the concept of XAI for smart cities, various XAI technology use cases, challenges, applications, possible alternative solutions, and current and future research enhancements. Research projects and activities, including standardization efforts toward developing XAI for smart cities, are outlined in detail. The lessons learned from state-of-the-art research are summarized, and various technical challenges are discussed to shed new light on future research possibilities. The presented study on XAI for smart cities is a first-of-its-kind, rigorous, and detailed study to assist future researchers in implementing XAI-driven systems, architectures, and applications for smart cities
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| 6. |
- Nguyen, Thanh N, et al.
(författare)
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Global Impact of the COVID-19 Pandemic on Stroke Volumes and Cerebrovascular Events: A 1-Year Follow-up.
- 2023
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Ingår i: Neurology. - 1526-632X. ; 100:4
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Tidskriftsartikel (refereegranskat)abstract
- Declines in stroke admission, IV thrombolysis (IVT), and mechanical thrombectomy volumes were reported during the first wave of the COVID-19 pandemic. There is a paucity of data on the longer-term effect of the pandemic on stroke volumes over the course of a year and through the second wave of the pandemic. We sought to measure the effect of the COVID-19 pandemic on the volumes of stroke admissions, intracranial hemorrhage (ICH), IVT, and mechanical thrombectomy over a 1-year period at the onset of the pandemic (March 1, 2020, to February 28, 2021) compared with the immediately preceding year (March 1, 2019, to February 29, 2020).We conducted a longitudinal retrospective study across 6 continents, 56 countries, and 275 stroke centers. We collected volume data for COVID-19 admissions and 4 stroke metrics: ischemic stroke admissions, ICH admissions, IVT treatments, and mechanical thrombectomy procedures. Diagnoses were identified by their ICD-10 codes or classifications in stroke databases.There were 148,895 stroke admissions in the 1 year immediately before compared with 138,453 admissions during the 1-year pandemic, representing a 7% decline (95% CI [95% CI 7.1-6.9]; p < 0.0001). ICH volumes declined from 29,585 to 28,156 (4.8% [5.1-4.6]; p < 0.0001) and IVT volume from 24,584 to 23,077 (6.1% [6.4-5.8]; p < 0.0001). Larger declines were observed at high-volume compared with low-volume centers (all p < 0.0001). There was no significant change in mechanical thrombectomy volumes (0.7% [0.6-0.9]; p = 0.49). Stroke was diagnosed in 1.3% [1.31-1.38] of 406,792 COVID-19 hospitalizations. SARS-CoV-2 infection was present in 2.9% ([2.82-2.97], 5,656/195,539) of all stroke hospitalizations.There was a global decline and shift to lower-volume centers of stroke admission volumes, ICH volumes, and IVT volumes during the 1st year of the COVID-19 pandemic compared with the prior year. Mechanical thrombectomy volumes were preserved. These results suggest preservation in the stroke care of higher severity of disease through the first pandemic year.This study is registered under NCT04934020.
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| 7. |
- Pandya, Sharnil, et al.
(författare)
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A Study of the Impacts of Air Pollution on the Agricultural Community and Yield Crops (Indian Context)
- 2022
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 14:20
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Tidskriftsartikel (refereegranskat)abstract
- Air pollution has been an vital issue throughout the 21st century, and has also significantly impacted the agricultural community, especially farmers and yield crops. This work aims to review air-pollution research to understand its impacts on the agricultural community and yield crops, specifically in developing countries, such as India. The present work highlights various aspects of agricultural damage caused by the impacts of air pollution. Furthermore, in the undertaken study, a rigorous and detailed discussion of state-wise and city-wise yield-crop losses caused by air pollution in India and its impacts has been performed. To represent air-pollution impacts, the color-coding-based AQI (Air Quality Index) risk-classification metrics have been used to represent AQI variations in India's agrarian states and cities. Finally, recent impacts of air pollution concerning AQI variations for May 2019 to February 2020, Seasonal AQI variations, impacts of PM2.5 , and PM10 in various agrarian states and India cities are presented using various tabular and graphical representations.
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| 8. |
- Pandya, Sharnil, Researcher, 1984-, et al.
(författare)
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COUNTERSAVIOR : AIoMT and IIoT enabled Adaptive Virus Outbreak Discovery Framework for Healthcare Informatics
- 2023
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662 .- 2372-2541. ; 10:4, s. 4202-4212
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Tidskriftsartikel (refereegranskat)abstract
- In the current Pandemic, global issues have caused health issues as well as economic downturns. At the beginning of every novel virus outbreak, lockdown is the best possible weapon to reduce the virus spread and save human life as the medical diagnosis followed by treatment and clinical approval takes significant time. The proposed COUNTERSAVIOR system aims at an Artificial Intelligence of Medical Things (AIoMT), and an edge line computing enabled and Big data analytics supported tracing and tracking approach that consumes GPS spatiotemporal data. COUNTERSAVIOR will be a better scientific tool to handle any virus outbreak. The proposed research discovers the prospect of applying an individual’s mobility to label mobility streams and forecast a virus such as COVID-19 pandemic transmission. The proposed system is the extension of the previously proposed COUNTERACT system. The proposed system can also identify the alternative saviour path concerning the confirmed subject’s cross-path using GPS data to avoid the possibility of infections. In the undertaken study, dynamic meta direct and indirect transmission, meta behaviour, and meta transmission saviour models are presented. In conducted experiments, the machine learning and deep learning methodologies have been used with the recorded historical location data for forecasting the behaviour patterns of confirmed and suspected individuals and a robust comparative analysis is also presented. The proposed system produces a report specifying people that have been exposed to the virus and notifying users about available pandemic saviour paths. In the end, we have represented 3D tracker movements of individuals, 3D contact analysis of COVID-19 and suspected individuals for 24 hours, forecasting and risk classification of COVID-19, suspected and safe individuals.
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| 9. |
- Pandya, Sharnil, Researcher, 1984-, et al.
(författare)
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Federated learning for smart cities : A comprehensive survey
- 2023
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Ingår i: Sustainable Energy Technologies and Assessments. - : Elsevier. - 2213-1388 .- 2213-1396. ; 55
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Tidskriftsartikel (refereegranskat)abstract
- With the advent of new technologies such as the Artificial Intelligence of Things (AIoT), big data, fog computing, and edge computing, smart city applications have suffered from issues, such as leakage of confidential and sensitive information. To envision smart cities, it will be necessary to integrate federated learning (FL) with smart city applications. FL integration with smart city applications can provide privacy preservation and sensitive information protection. In this paper, we present a comprehensive overview of the current and future developments of FL for smart cities. Furthermore, we highlight the societal, industrial, and technological trends driving FL for smart cities. Then, we discuss the concept of FL for smart cities, and numerous FL integrated smart city applications, including smart transportation systems, smart healthcare, smart grid, smart governance, smart disaster management, smart industries, and UAVs for smart city monitoring, as well as alternative solutions and research enhancements for the future. Finally, we outline and analyze various research challenges and prospects for the development of FL for smart cities.
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| 10. |
- Pandya, Sharnil, Researcher, 1984-, et al.
(författare)
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InfusedHeart : A Novel Knowledge-Infused Learning Framework for Diagnosis of Cardiovascular Events
- 2024
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Ingår i: IEEE Transactions on Computational Social Systems. - : IEEE. - 2329-924X .- 2373-7476. ; 11:3, s. 3060-3069
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Tidskriftsartikel (refereegranskat)abstract
- In the undertaken study, we have used a customized dataset termed "Cardiac-200'' and the benchmark dataset "PhysioNet.'' which contains 1500 heartbeat acoustic event samples (without augmentation) and 1950 samples (with augmentation) heartbeat acoustic events such as normal, murmur, extrasystole, artifact, and other unlabeled heartbeat acoustic events. The primary reason for designing a customized dataset, "cardiac-200,'' is to balance the total number of samples into categories such as normal and abnormal heartbeat acoustic events. The average duration of the recorded heartbeat acoustic events is 10-12 s. In the undertaken study, we have analyzed and evaluated various heartbeat acoustic events using audio processing libraries such as Chromagram, Chroma-cq, Chroma-short-time Fourier transform (STFT), Chroma-cqt, and Chroma-cens to extract more information from the recorded heartbeat sound signals. The noise removal process has been carried out using local binary pattern (LBP) methodology. The noise-robust heartbeat acoustic images are classified using long short-term memory (LSTM)-convolutional neural network (CNN), recurrent neural network (RNN), LSTM, Bi-LSTM, CNN, K-means Clustering, and support vector machine (SVM) methods. The obtained results have shown that the proposed InfusedHeart Framework had outclassed all the other customized machine learning and deep learning approaches such as RNN, LSTM, Bi-LSTM, CNN, K-means Clustering, and SVM-based classification methodologies. The proposed Knowledge-infused Learning Framework has achieved an accuracy of 89.36% (without augmentation), 93.38% (with augmentation), and a standard deviation of 10.64 (without augmentation), and 6.62 (with augmentation). Furthermore, the proposed framework has been tested for various signal-to-noise ratio conditions such as SignaltoNoiseRatio0, SignaltoNoiseRatio3, SignaltoNoiseRatio6, SignaltoNoiseRatio9, SignaltoNoiseRatio12, SignaltoNoiseRatio15, and SignaltoNoiseRatio18. In the end, we have shown a detailed comparison of texture and without texture approaches and have discussed future enhancements and prospective ways for future directions.
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| 11. |
- Pavithra, Chokkakula L.P., et al.
(författare)
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Graphene Oxide Reinforced Magnetic FeCoNiCuZn High Entropy Alloy through Electrodeposition
- 2022
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Ingår i: Journal of the Electrochemical Society. - : The Electrochemical Society. - 1945-7111 .- 0013-4651. ; 169:2
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Tidskriftsartikel (refereegranskat)abstract
- The production of high entropy alloy-based nanocomposites is an exciting yet challenging area in terms of its scalability and industrial applications. Here we developed graphene oxide (GO) reinforced FeCoNiCuZn high entropy alloy (HEA) nanocomposites through an electrochemical approach using aqueous medium in a single step. Transmission electron microscopy observations confirmed uniformly distributed nanocrystalline dual FCC phase quinary alloy nanoparticles throughout the GO layers. On the other hand, the presence of GO affects the electrochemical reduction of multiple elements during alloy formation in the deposition process, which often leads to dual phases with slight deviations in alloy composition, unlike the pure metal-GO composites. Additionally, incorporation of GO has not shown any effect on the ferromagnetic nature of FeCoNiCuZn HEA with saturation magnetization (Ms) ∼ 43.5 emu g−1. The obtained saturation magnetization is relatively higher compared to the existing reported magnetic nanoparticles with GO. Hence, this technique shows its potential applicability and provides an old technique yet a new approach for synthesizing GO-HEA nanocomposites for various magnetic applications.
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| 12. |
- Reddy Abbu,, Spuran, et al.
(författare)
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Cost Model for remanufacturing
- 2022
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Ingår i: SPSS2022 : Proceedings of the 10th Swedish Production Symposium - Proceedings of the 10th Swedish Production Symposium. - 2352-7528 .- 2352-751X. - 9781643682693 - 9781643682686 ; 21, s. 267-278
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Konferensbidrag (refereegranskat)abstract
- Over the years, remanufacturing practices have grown in popularity in industrial areas. It is one of the most effective ways to keep products out of landfills by turning end-of-life items into usable products. However, the available literature on cost models is limited or specific to a product. A general cost model for remanufacturing is presented in this paper to understand the major costs involved in the process to aid decision-making. A model is developed based on activity-based costing and performance-based costing by considering both the activities that occur during the process and their performance. As a result of using the proposed model, it is easier to monitor the costs of each activity, changes in performance, and understand the resulting impact on the cost of finished products.
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| 13. |
- Victor, Nancy, et al.
(författare)
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Federated learning for iout : Concepts, applications, challenges and future directions
- 2022
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Ingår i: IEEE Internet of Things Magazine (IoT). - 2576-3180 .- 2576-3199. ; 5:4
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Tidskriftsartikel (refereegranskat)abstract
- Internet of Underwater Things (IoUT) have gained rapid momentum over the past decade with applications spanning from environmental monitoring and exploration, defence applications, etc. The traditional IoUT systems use machine learning (ML) approaches which cater the needs of reliability, efficiency and timeliness. However, an extensive review of the various studies conducted highlight the significance of data privacy and security in IoUT frameworks as a predominant factor in achieving desired outcomes in mission critical applications. Federated learning (FL) is a secured, decentralized framework which is a recent development in ML, that can help in fulfilling the challenges faced by conventional ML approaches in IoUT. This article presents an overview of the various applications of FL in IoUT, its challenges, open issues and indicates direction of future research prospects.
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| 14. |
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| 15. |
- Kanai, M, et al.
(författare)
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- 2023
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swepub:Mat__t
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| 16. |
- Abrishami, Vahid, et al.
(författare)
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Localized reconstruction in Scipion expedites the analysis of symmetry mismatches in cryo-EM data
- 2021
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Ingår i: Progress in Biophysics and Molecular Biology. - : Elsevier BV. - 0079-6107 .- 1873-1732. ; 160, s. 43-52
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Forskningsöversikt (refereegranskat)abstract
- Technological advances in transmission electron microscopes and detectors have turned cryogenic electron microscopy (cryo-EM) into an essential tool for structural biology. A commonly used cryo-EM data analysis method, single particle analysis, averages hundreds of thousands of low-dose images of individual macromolecular complexes to determine a density map of the complex. The presence of symmetry in the complex is beneficial since each projection image can be assigned to multiple views of the complex. However, data processing that applies symmetry can average out asymmetric features and consequently data analysis methods are required to resolve asymmetric structural features. Scipion is a cryo-EM image processing framework that integrates functions from different image processing packages as plugins. To extend its functionality for handling symmetry mismatches, we present here a Scipion plugin termed LocalRec implementing the localized reconstruction method. When tested on an adenovirus data set, the plugin enables resolving the symmetry-mismatched trimeric fibre bound to the five-fold vertices of the capsid. Furthermore, it improves the structure determination of the icosahedral capsid by dealing with the defocus gradient across the particle. LocalRec is expected to be widely applicable in a range of cryo-EM investigations of flexible and symmetry mismatched complexes.
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| 17. |
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| 18. |
- Adler, Anneli, et al.
(författare)
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Lignin-first biorefining of Nordic poplar to produce cellulose fibers could displace cotton production on agricultural lands
- 2022
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Ingår i: Joule. - : Elsevier BV. - 2542-4351. ; 6:8, s. 1845-1858
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Tidskriftsartikel (refereegranskat)abstract
- Here, we show that lignin-first biorefining of poplar can enable the production of dissolving cellulose pulp that can produce regenerated cellulose, which could substitute cotton. These results in turn indicate that agricultural land dedicated to cotton could be reclaimed for food production by extending poplar plantations to produce textile fibers. Based on climate-adapted poplar clones capable of growth on marginal lands in the Nordic region, we estimate an environmentally sustainable annual biomass production of ∼11 tonnes/ha. At scale, lignin-first biorefining of this poplar could annually generate 2.4 tonnes/ha of dissolving pulp for textiles and 1.1 m3 biofuels. Life cycle assessment indicates that, relative to cotton production, this approach could substantially reduce water consumption and identifies certain areas for further improvement. Overall, this work highlights a new value chain to reduce the environmental footprint of textiles, chemicals, and biofuels while enabling land reclamation and water savings from cotton back to food production.
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| 19. |
- Amin, Muhammad Umair, et al.
(författare)
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Ultrasound-Responsive Smart Drug Delivery System of Lipid Coated Mesoporous Silica Nanoparticles
- 2021
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Ingår i: Pharmaceutics. - : MDPI. - 1999-4923. ; 13:9
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Tidskriftsartikel (refereegranskat)abstract
- The immediate release of chemotherapeutics at the target site, along with no premature release in circulation is always challenging. The purpose of this study was to develop a stimuli responsive drug delivery system, composed of lipid supported mesoporous silica nanoparticles (MSNPs) for triggered drug release at the target site and simultaneously avoiding the premature release. MSNPs with a higher drug loading capacity and very slow release were designed so as to enhance release by FDA approved US-irradiation. Doxorubicin, as a model drug, and perfluoropentane (PFP) as a US responsive material, were entrapped in the porous structure of MSNPs. Lipid coating enhanced the cellular uptake and in addition provided a gatekeeping effect at the pore opening to reduce premature release. The mechanical and thermal effects of US induced the conversion of liquid PFP to a gaseous form that was able to rupture the lipid layer, resulting in triggered drug release. The prolonged stability profile and non-toxic behavior made them suitable candidate for the delivery of anticancer drugs. This smart system, with the abilities of better cellular uptake and higher cytotoxic effects on US-irradiation, would be a good addition to the applied side of chemotherapeutic advanced drug delivery systems.
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| 20. |
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| 21. |
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| 22. |
- Anjaneya Reddy, Yuvarajendra, et al.
(författare)
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Experimental dataset investigation of deep recurrent optical flow learning for particle image velocimetry: flow past a circular cylinder
- 2024
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Ingår i: Measurement science and technology. - : Institute of Physics (IOP). - 0957-0233 .- 1361-6501. ; 35:8
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Tidskriftsartikel (refereegranskat)abstract
- Current optical flow-based neural networks for particle image velocimetry (PIV) are largely trained on synthetic datasets emulating real-world scenarios. While synthetic datasets provide greater control and variation than what can be achieved using experimental datasets for supervised learning, it requires a deeper understanding of how or what factors dictate the learning behaviors of deep neural networks for PIV. In this study, we investigate the performance of the recurrent all-pairs field transforms-PIV (RAFTs-PIV) network, the current state-of-the-art deep learning architecture for PIV, by testing it on unseen experimentally generated datasets. The results from RAFT-PIV are compared with a conventional cross-correlation-based method, Adaptive PIV. The experimental PIV datasets were generated for a typical scenario of flow past a circular cylinder in a rectangular channel. These test datasets encompassed variations in particle diameters, particle seeding densities, and flow speeds, all falling within the parameter range used for training RAFT-PIV. We also explore how different image pre-processing techniques can impact and potentially enhance the performance of RAFT-PIV on real-world datasets. Thorough testing with real-world experimental PIV datasets reveals the resilience of the optical flow-based method's variations to PIV hyperparameters, in contrast to the conventional PIV technique. The ensemble-averaged root mean squared errors between the RAFT-PIV and Adaptive PIV estimations generally range between 0.5–2 (px) and show a slight reduction as particle densities increase or Reynolds numbers decrease. Furthermore, findings indicate that employing image pre-processing techniques to enhance input particle image quality does not improve RAFT-PIV predictions; instead, it incurs higher computational costs and impacts estimations of small-scale structures.
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| 23. |
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| 24. |
- Arikumar, K. S., et al.
(författare)
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FL-PMI : Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems
- 2022
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 22:4
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Tidskriftsartikel (refereegranskat)abstract
- Recent technological developments, such as the Internet of Things (IoT), artificial intelligence, edge, and cloud computing, have paved the way in transforming traditional healthcare systems into smart healthcare (SHC) systems. SHC escalates healthcare management with increased efficiency, convenience, and personalization, via use of wearable devices and connectivity, to access information with rapid responses. Wearable devices are equipped with multiple sensors to identify a person's movements. The unlabeled data acquired from these sensors are directly trained in the cloud servers, which require vast memory and high computational costs. To overcome this limitation in SHC, we propose a federated learning-based person movement identification (FL-PMI). The deep reinforcement learning (DRL) framework is leveraged in FL-PMI for auto-labeling the unlabeled data. The data are then trained using federated learning (FL), in which the edge servers allow the parameters alone to pass on the cloud, rather than passing vast amounts of sensor data. Finally, the bidirectional long short-term memory (BiLSTM) in FL-PMI classifies the data for various processes associated with the SHC. The simulation results proved the efficiency of FL-PMI, with 99.67% accuracy scores, minimized memory usage and computational costs, and reduced transmission data by 36.73%.
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| 25. |
- Ashok, Nrupathunga, et al.
(författare)
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DEVELOPMENT OF TEST ENVIRONMENTS FOR REVERSE ASSIST FUNCTIONS AS APPLIED TO AN A-DOUBLE VEHICLE COMBINATION
- 2021
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Ingår i: The 16th International Symposium on Heavy Vehicle Transport & Technology.
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Konferensbidrag (refereegranskat)abstract
- High-capacity transport vehicles reduce costs and improve efficiency. Long vehicle combinations such as an A-double combination vehicle (Tractor + semitrailer + dolly + semitrailer) improve transportation efficiency but they are extremely difficult to manoeuvre in tight spaces and in the reverse direction. This document summarizes developing environments to test reverse assist functions as applied to the A-double combination vehicle. These environments create a rapid prototyping platform consisting of a virtual and a scaled environment to test and validate controller concepts. The behaviour of the plant model in the virtual environment, the scaled vehicle model and the plant model in VTM (Volvo Truck Model) are studied and compared. A proportional controller is developed to test the environments and evaluate the process of concept development using the rapid prototype platform. The controller performance is evaluated and a possibility of incorporating integral controller is discussed.
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| 26. |
- Avula, Ramana Reddy, et al.
(författare)
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Adversarial Inference Control in Cyber-Physical Systems : A Bayesian Approach With Application to Smart Meters
- 2024
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers Inc.. - 2169-3536. ; 12, s. 24933-24948
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Tidskriftsartikel (refereegranskat)abstract
- With the emergence of cyber-physical systems (CPSs) in utility systems like electricity, water, and gas networks, data collection has become more prevalent. While data collection in these systems has numerous advantages, it also raises concerns about privacy as it can potentially reveal sensitive information about users. To address this issue, we propose a Bayesian approach to control the adversarial inference and mitigate the physical-layer privacy problem in CPSs. Specifically, we develop a control strategy for the worst-case scenario where an adversary has perfect knowledge of the user’s control strategy. For finite state-space problems, we derive the fixed-point Bellman’s equation for an optimal stationary strategy and discuss a few practical approaches to solve it using optimization-based control design. Addressing the computational complexity, we propose a reinforcement learning approach based on the Actor-Critic architecture. To also support smart meter privacy research, we present a publicly accessible ’Co-LivEn’ dataset with comprehensive electrical measurements of appliances in a co-living household. Using this dataset, we benchmark the proposed reinforcement learning approach. The results demonstrate its effectiveness in reducing privacy leakage. Our work provides valuable insights and practical solutions for managing adversarial inference in cyber-physical systems, with a particular focus on enhancing privacy in smart meter applications.
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| 27. |
- Avula, Ramana Reddy, 1993-
(författare)
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Towards Realistic Smart Meter Privacy against Bayesian Inference
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Smart meters, now an essential component of modern power grids, allow energy providers to remotely monitor users' energy consumption in near real-time. While this technology offers numerous advantages for energy management and system efficiency, it also poses significant privacy concerns. High-resolution energy consumption data can reveal sensitive information about users' routines and activities, thus potentially jeopardizing their privacy. In particular, research has demonstrated that Bayesian inference attacks can effectively disaggregate smart meter data to deduce household appliance states and subsequently obtain sensitive user information.This thesis investigates the use of energy storage systems to protect smart meter data privacy against Bayesian inference attacks. Although several methods have been proposed in the literature that employ energy storage systems for this purpose, many rely on ideal assumptions such as lossless energy storage systems. To address this issue, a data-driven energy storage model that considers energy losses and capacity degradation has been proposed. Privacy leakage is quantified in terms of Bayesian risk, and control strategies are devised to minimize Bayesian risk while accounting for the energy storage system's operational constraints and economic implications. The findings reveal that non-idealities in energy storage systems significantly affect the privacy-preserving performance of control strategies. Moreover, incorporating degradation losses in the design of privacy-enhancing control strategies considerably improves battery life, albeit with some privacy loss.Taking into account the non-idealities of energy storage, this thesis introduces novel privacy-preserving control strategies using various adversarial models, which are classified based on their knowledge of the control system. These models include controller-aware and controller-unaware adversaries employing sequential hypothesis testing or maximum a posteriori detection. The proposed control strategies are evaluated through numerical simulations using real data and emulated energy storage systems. Additionally, the thesis provides a reference dataset of appliance power consumption, featuring detailed electrical measurements to support future smart meter privacy research. In summary, this work offers valuable insights and practical solutions for managing adversarial inference in cyber-physical systems, with potential applications extending to other sensor networks beyond smart meters.
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| 28. |
- Babu, Challa, et al.
(författare)
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Performance analysis of flat plate hybrid PV/thermal configurations
- 2022
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Ingår i: AIP Conference Proceedings. - : American Institute of Physics (AIP). - 9780735443570
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Konferensbidrag (refereegranskat)abstract
- In the recent times many hybrid renewable energy sources are developed. In that, hybrid PV/Thermal gains the more attention than other hybrid sources. In the present work, made a performance analysis of different PV/Thermal configurations. The flat plate configurations have the more feasibility for the domestic applications than the concentrated type. In this paper, liquid, air, nano fluid, phase change material and Thermoelectric generator type configurations are presented. The performance analysis of all configurations done with energy output generation and efficiency of the system.
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| 29. |
- Babu, Challa, et al.
(författare)
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Power Monitoring and Control System for Medium Voltage Smart Grid Using IoT
- 2020
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Ingår i: IOP Conference Series. - : IOP Publishing.
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Konferensbidrag (refereegranskat)abstract
- This paper presents a power monitoring and control system for a medium voltage smart grid system. The smart grid interconnects the power sources between solar PV panel and 220V distribution network. This system consisting major components of inverters, measuring meters, solar charge controllers, relays, Arduino NANO and Raspberry Pi. The Current, power and energy readings are duly recorded. The Internet of Things (IoT) plays the vital role in the data communication between the sensors and electric power system. The voltage and current sensor data is used for the protection of power system network. In the traditional systems only the communication is uni-directional. The reliability of power supply is increased by used the bi directional network communication medium such as IoT. The tabulated results of voltage levels between 203.5V up to 212.8V shows the feasibility and effectiveness of proposed design. The proposed IoT model demonstrated the bidirectional communication from the sensors to the control unit and vice versa.
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| 30. |
- Balachander, S, et al.
(författare)
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Are There Familial Patterns of Symptom Dimensions in Obsessive-Compulsive Disorder?
- 2021
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Ingår i: Frontiers in psychiatry. - : Frontiers Media SA. - 1664-0640. ; 12, s. 651196-
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Tidskriftsartikel (refereegranskat)abstract
- Background:Obsessive-compulsive disorder (OCD) is a heterogeneous illness, and emerging evidence suggests that different symptom dimensions may have distinct underlying neurobiological mechanisms. We aimed to look for familial patterns in the occurrence of these symptom dimensions in a sample of families with at least two individuals affected with OCD.Methods:Data from 153 families (total number of individuals diagnosed with DSM-5 OCD = 330) recruited as part of the Accelerator Program for Discovery in Brain Disorders using Stem Cells (ADBS) was used for the current analysis. Multidimensional Item Response Theory (IRT) was used to extract dimensional scores from the Yale-Brown Obsessive-Compulsive Scale (YBOCS) checklist data. Using linear mixed-effects regression models, intra-class correlation coefficients (ICC), for each symptom dimension, and within each relationship type were estimated.Results:IRT yielded a four-factor solution with Factor 1 (Sexual/Religious/Aggressive), Factor 2 (Doubts/Checking), Factor 3 (Symmetry/Arranging), and Factor 4 (Contamination/Washing). All except for Factor 1 were found to have significant ICCs, highest for Factor 3 (0.41) followed by Factor 4 (0.29) and then Factor 2 (0.27). Sex-concordant dyads were found to have higher ICC values than discordant ones, for all the symptom dimensions. No major differences in the ICC values between parent-offspring and sib-pairs were seen.Conclusions:Our findings indicate that there is a high concordance of OCD symptom dimensions within multiplex families. Symptom dimensions of OCD might thus have significant heritability. In view of this, future genetic and neurobiological studies in OCD should include symptom dimensions as a key parameter in their analyses.
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| 31. |
- Barlow, Nicholas, et al.
(författare)
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Macrocyclic Peptidomimetics as Inhibitors of Insulin-Regulated Aminopeptidase (IRAP)
- 2020
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Ingår i: RSC Medicinal chemistry. - : Royal Society of Chemistry (RSC). - 2632-8682. ; 11:2, s. 234-244
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Tidskriftsartikel (refereegranskat)abstract
- Macrocyclic analogues of the linear hexapeptide, angiotensin IV (AngIV) have proved to be potent inhibitors of insulin-regulated aminopeptidase (IRAP, oxytocinase, EC 3.4.11.3). Along with higher affinity, macrocycles may also offer better metabolic stability, membrane permeability and selectivity, however predicting the outcome of particular cycle modifications is challenging. Here we describe the development of a series of macrocyclic IRAP inhibitors with either disulphide, olefin metathesis or lactam bridges and variations of ring size and other functionality. The binding mode of these compounds is proposed based on molecular dynamics analysis. Estimation of binding affinities (∆G) and relative binding free energies (∆∆G) with the linear interaction energy (LIE) method and free energy perturbation (FEP) method showed good general agreement with the observed inhibitory potency. Experimental and calculated data highlight the cumulative importance of an intact N-terminal peptide, the specific nature of the macrocycle, the phenolic oxygen and the C-terminal functionality.
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| 32. |
- Barreveld, Antje M., et al.
(författare)
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An artificial intelligence-powered, patient-centric digital tool for self-management of chronic pain : a prospective, multicenter clinical trial
- 2023
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Ingår i: Pain Medicine (United States). - 1526-2375. ; 24:9, s. 1100-1110
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Tidskriftsartikel (refereegranskat)abstract
- Objective: To investigate how a behavioral health, artificial intelligence (AI)-powered, digital self-management tool affects the daily functions in adults with chronic back and neck pain. Design: Eligible subjects were enrolled in a 12-week prospective, multicenter, single-arm, open-label study and instructed to use the digital coach daily. Primary outcome was a change in Patient-Reported Outcomes Measurement Information Systems (PROMIS) scores for pain interference. Secondary outcomes were changes in PROMIS physical function, anxiety, depression, pain intensity scores and pain catastrophizing scale (PCS) scores. Methods: Subjects logged daily activities, using PainDrainerTM, and data analyzed by the AI engine. Questionnaire and web-based data were collected at 6 and 12 weeks and compared to subjects' baseline. Results: Subjects completed the 6- (n = 41) and 12-week (n = 34) questionnaires. A statistically significant Minimal Important Difference (MID) for pain interference was demonstrated in 57.5% of the subjects. Similarly, MID for physical function was demonstrated in 72.5% of the subjects. A pre- to post-intervention improvement in depression score was also statistically significant, observed in 100% of subjects, as was the improvement in anxiety scores, evident in 81.3% of the subjects. PCS mean scores was also significantly decreased at 12 weeks. Conclusion: Chronic pain self-management, using an AI-powered, digital coach anchored in behavioral health principles significantly improved subjects' pain interference, physical function, depression, anxiety, and pain catastrophizing over the 12-week study period.
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| 33. |
- Barth, Henrik, 1971-, et al.
(författare)
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From Traditional Manufacturing to Digital Manufacturing : Two Swedish Case Studies
- 2023
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Ingår i: Innovation - Research and Development for Human, Economic and Institutional Growth. - Rijeka : InTech. - 9781837689965 - 9781837689972 - 9781837689989
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Bokkapitel (refereegranskat)abstract
- Digital manufacturing can produce new and advanced tools more rapidly and at lower cost than traditional manufacturing. This new technology means manufacturers need to develop innovative business models adapted to this change in the manufacturing landscape. With digital manufacturing, companies have both an opportunity and a challenge. They can enter new markets where large-scale production provides competitive advantage. They can enter niche markets that become more attractive as old boundaries and structures lose relevance. Yet their additive manufactured components must meet the same standards set for conventional manufactured components. However, we know little about how companies manage this change as they make the transition from traditional manufacturing to digital manufacturing. This chapter presents two co-creation digital manufacturing projects between university researchers and Swedish companies. In each project, the goal was to develop sustainable and efficient digital production methods that offer tailor-made product solutions. Various technical methods used in the projects are described as materials, and prototypes are developed, tested, and analyzed.
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| 34. |
- Bathi, Jejal Reddy, et al.
(författare)
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Behavior of engineered nanoparticles in aquatic environmental samples : Current status and challenges
- 2021
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Ingår i: Science of the Total Environment. - : Elsevier. - 0048-9697 .- 1879-1026. ; 793
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Forskningsöversikt (refereegranskat)abstract
- The increasing use of engineered nanoparticles (ENPs) in consumer products has led to their increased presence in natural water systems. Here, we present a critical overview of the studies that analyzed the fate and transport behavior of ENPs using real environmental samples. We focused on cerium dioxide, titanium dioxide, silver, carbon nanotubes, and zinc oxide, the widely used ENPs in consumer products. Under field scale settings, the transformation rates of ENPs and subsequently their physicochemical properties (e.g., toxicity and bioavailability) are primarily influenced by the modes of interactions among ENPs and natural organic matter. Other typical parameters include factors related to water chemistry, hydrodynamics, and surface and electronic properties of ENPs. Overall, future nanomanufacturing processes should fully consider the health, safety, and environmental impacts without compromising the functionality of consumer products.
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| 35. |
- Bauer, M., et al.
(författare)
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Association between polarity of first episode and solar insolation in bipolar I disorder
- 2022
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Ingår i: Journal of Psychosomatic Research. - : Elsevier BV. - 0022-3999 .- 1879-1360. ; 160
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Circadian rhythm disruption is commonly observed in bipolar disorder (BD). Daylight is the most powerful signal to entrain the human circadian clock system. This exploratory study investigated if solar insolation at the onset location was associated with the polarity of the first episode of BD I. Solar insolation is the amount of electromagnetic energy from the Sun striking a surface area of the Earth. Methods: Data from 7488 patients with BD I were collected at 75 sites in 42 countries. The first episode occurred at 591 onset locations in 67 countries at a wide range of latitudes in both hemispheres. Solar insolation values were obtained for every onset location, and the ratio of the minimum mean monthly insolation to the maximum mean monthly insolation was calculated. This ratio is largest near the equator (with little change in solar insolation over the year), and smallest near the poles (where winter insolation is very small compared to summer insolation). This ratio also applies to tropical locations which may have a cloudy wet and clear dry season, rather than winter and summer. Results: The larger the change in solar insolation throughout the year (smaller the ratio between the minimum monthly and maximum monthly values), the greater the likelihood the first episode polarity was depression. Other associated variables were being female and increasing percentage of gross domestic product spent on country health expenditures. (All coefficients: P ≤ 0.001). Conclusion: Increased awareness and research into circadian dysfunction throughout the course of BD is warranted.
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| 36. |
- Bauer, M., et al.
(författare)
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Exploratory study of ultraviolet B (UVB) radiation and age of onset of bipolar disorder
- 2023
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Ingår i: International Journal of Bipolar Disorders. - 2194-7511. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundSunlight contains ultraviolet B (UVB) radiation that triggers the production of vitamin D by skin. Vitamin D has widespread effects on brain function in both developing and adult brains. However, many people live at latitudes (about > 40 N or S) that do not receive enough UVB in winter to produce vitamin D. This exploratory study investigated the association between the age of onset of bipolar I disorder and the threshold for UVB sufficient for vitamin D production in a large global sample.MethodsData for 6972 patients with bipolar I disorder were obtained at 75 collection sites in 41 countries in both hemispheres. The best model to assess the relation between the threshold for UVB sufficient for vitamin D production and age of onset included 1 or more months below the threshold, family history of mood disorders, and birth cohort. All coefficients estimated at P <= 0.001.ResultsThe 6972 patients had an onset in 582 locations in 70 countries, with a mean age of onset of 25.6 years. Of the onset locations, 34.0% had at least 1 month below the threshold for UVB sufficient for vitamin D production. The age of onset at locations with 1 or more months of less than or equal to the threshold for UVB was 1.66 years younger.ConclusionUVB and vitamin D may have an important influence on the development of bipolar disorder. Study limitations included a lack of data on patient vitamin D levels, lifestyles, or supplement use. More study of the impacts of UVB and vitamin D in bipolar disorder is needed to evaluate this supposition.
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| 37. |
- Bauer, M., et al.
(författare)
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Variations in seasonal solar insolation are associated with a history of suicide attempts in bipolar I disorder
- 2021
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Ingår i: International Journal of Bipolar Disorders. - : Springer Science and Business Media LLC. - 2194-7511. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- Background Bipolar disorder is associated with circadian disruption and a high risk of suicidal behavior. In a previous exploratory study of patients with bipolar I disorder, we found that a history of suicide attempts was associated with differences between winter and summer levels of solar insolation. The purpose of this study was to confirm this finding using international data from 42% more collection sites and 25% more countries. Methods Data analyzed were from 71 prior and new collection sites in 40 countries at a wide range of latitudes. The analysis included 4876 patients with bipolar I disorder, 45% more data than previously analyzed. Of the patients, 1496 (30.7%) had a history of suicide attempt. Solar insolation data, the amount of the sun's electromagnetic energy striking the surface of the earth, was obtained for each onset location (479 locations in 64 countries). Results This analysis confirmed the results of the exploratory study with the same best model and slightly better statistical significance. There was a significant inverse association between a history of suicide attempts and the ratio of mean winter insolation to mean summer insolation (mean winter insolation/mean summer insolation). This ratio is largest near the equator which has little change in solar insolation over the year, and smallest near the poles where the winter insolation is very small compared to the summer insolation. Other variables in the model associated with an increased risk of suicide attempts were a history of alcohol or substance abuse, female gender, and younger birth cohort. The winter/summer insolation ratio was also replaced with the ratio of minimum mean monthly insolation to the maximum mean monthly insolation to accommodate insolation patterns in the tropics, and nearly identical results were found. All estimated coefficients were significant at p < 0.01. Conclusion A large change in solar insolation, both between winter and summer and between the minimum and maximum monthly values, may increase the risk of suicide attempts in bipolar I disorder. With frequent circadian rhythm dysfunction and suicidal behavior in bipolar disorder, greater understanding of the optimal roles of daylight and electric lighting in circadian entrainment is needed.
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| 38. |
- Bera, Anup Kumar, et al.
(författare)
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Morphology induced large magnetic anisotropy in obliquely grown nanostructured thin film on nanopatterned substrate
- 2022
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Ingår i: Applied Surface Science. - : Elsevier BV. - 0169-4332 .- 1873-5584. ; 581
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Tidskriftsartikel (refereegranskat)abstract
- The artificial tailoring of magnetic anisotropy by manipulating surface and interface morphology is attracting widespread interest for its application in spintronic and magnetic memory devices. Here oblique angle deposition on a nanopatterned rippled substrate is presented as a novel route of inducing large in-plane uniaxial magnetic anisotropy (UMA) in magnetic thin films. For this purpose, Cobalt films and rippled SiO2 substrates have been taken as a model system for the present study. Here, nanopatterned substrates are prepared by low energy ion beam erosion (IBE), above which films are deposited obliquely along and normal to the ripple directions. A clear anisotropy in the growth behavior has been observed due to the inhomogeneous in-plane organization of adatoms in the form of columns. The increased shadowing effect in the films deposited obliquely normal to the direction of the ripple patterns causes preferential coalescence of the columns along the substrate ripples, resulting in stronger in-plane UMA in the film. This peculiarity in magnetic behavior is addressed by considering the morphological anisotropy governed by enhanced shadowing effect, the shape anisotropy and the dipolar interactions among the magnetostatically coupled ripple structure.
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| 39. |
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| 40. |
- Bhimpuria, Rohan, et al.
(författare)
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Synthesis of styrene, phenyl acrylate and allyloxyphenyl-functionalized porphyrins, and preliminary exploration of their utility for assembling multiple porphyrin units
- 2023
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Ingår i: Journal of Porphyrins and Phthalocyanines. - : World Scientific. - 1088-4246 .- 1099-1409. ; 27:07N10, s. 1398-1408
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Tidskriftsartikel (refereegranskat)abstract
- Three free-base and two Zn(II) porphyrins carrying one alkene-substituted meso-aryl group and three solubilizing pentyl groups were prepared via mixed aldehyde-type syntheses. A meso p-allyloxyphenyl porphyrin was obtained via the corresponding 5-(p-allyloxyphenyl) dipyrromethane. The porphyrins were fully characterized using a combination of NMR spectroscopy, high-resolution mass spectrometry, and UV-Vis absorption and emission spectroscopies. Two of the free-base porphyrins were heated in the presence of AIBN or benzoyl peroxide as the initiator. The major isolated products of these reactions contained two porphyrin units based on the results of MALDI-MS and H-1 NMR analysis data, which was supported by the results of FTIR, UV-Vis absorption and emission spectroscopies, and gel permeation and liquid chromatographies. Additionally, the acrylamide-functionalized porphyrin could be attached to a polylysine scaffold under basic conditions. These results suggest that the olefinic handle can be used to assemble structures containing multiple porphyrinic macrocycles.
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| 41. |
- Binny, S., et al.
(författare)
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Distributed DoS Detection in IoT Networks Using Intelligent Machine Learning Algorithms
- 2021
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Ingår i: 2021 2nd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2021 - Proceedings.
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Konferensbidrag (refereegranskat)abstract
- The threat of a Distributed Denial of Service (DDoS) attack on web-based services and applications is grave. It only takes a few minutes for one of these attacks to cripple these services, making them unavailable to anyone. The problem has further persisted with the widespread adoption of insecure Internet of Things (IoT) devices across the Internet. In addition, many currently used rule-based detection systems are weak points for attackers. We conducted a comparative analysis of ML algorithms to detect and classify DDoS attacks in this paper. These classifiers compare Nave Bayes with J48 and Random Forest with ZeroR ML as well as other machine learning algorithms. It was found that using the PCA method, the optimal number of features could be found. ML has been implemented with the help of the WEKA tool.
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| 42. |
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| 43. |
- Bolla, Jani Reddy, et al.
(författare)
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A Mass-Spectrometry-Based Approach to Distinguish Annular and Specific Lipid Binding to Membrane Proteins
- 2020
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Ingår i: Angewandte Chemie International Edition. - : Wiley. - 1433-7851 .- 1521-3773. ; 59:9, s. 3523-3528
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Tidskriftsartikel (refereegranskat)abstract
- Membrane proteins engage in a variety of contacts with their surrounding lipids, but distinguishing between specifically bound lipids, and non-specific, annular interactions is a challenging problem. Applying native mass spectrometry to three membrane protein complexes with different lipid-binding properties, we explore the ability of detergents to compete with lipids bound in different environments. We show that lipids in annular positions on the presenilin homologue protease are subject to constant exchange with detergent. By contrast, detergent-resistant lipids bound at the dimer interface in the leucine transporter show decreased k(off) rates in molecular dynamics simulations. Turning to the lipid flippase MurJ, we find that addition of the natural substrate lipid-II results in the formation of a 1:1 protein-lipid complex, where the lipid cannot be displaced by detergent from the highly protected active site. In summary, we distinguish annular from non-annular lipids based on their exchange rates in solution.
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| 44. |
- Bose, Subhash, et al.
(författare)
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ASASSN-18am/SN 2018gk : an overluminous Type IIb supernova from a massive progenitor
- 2021
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Ingår i: Monthly notices of the Royal Astronomical Society. - : Oxford University Press (OUP). - 0035-8711 .- 1365-2966. ; 503:3, s. 3472-3491
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Tidskriftsartikel (refereegranskat)abstract
- ASASSN-18am/SN 2018gk is a newly discovered member of the rare group of luminous, hydrogen-rich supernovae (SNe) with a peak absolute magnitude of M-V approximate to -20 mag that is in between normal core-collapse SNe and superluminous SNe. These SNe show no prominent spectroscopic signatures of ejecta interacting with circumstellar material (CSM), and their powering mechanism is debated. ASASSN-18am declines extremely rapidly for a Type II SN, with a photospheric-phase decline rate of similar to 6.0 mag (100 d)(-1). Owing to the weakening of H I and the appearance of He I in its later phases, ASASSN-18am is spectroscopically a Type IIb SN with a partially stripped envelope. However, its photometric and spectroscopic evolution shows significant differences from typical SNe IIb. Using a radiative diffusion model, we find that the light curve requires a high synthesized Ni-56 mass M-Ni similar to 0.4 M-circle dot and ejecta with high kinetic energy E-kin = (7-10) x 10(51) erg. Introducing a magnetar central engine still requires M-Ni similar to 0.3 M-circle dot and E-kin = 3 x 10(51) erg. The high Ni-56 mass is consistent with strong iron-group nebular lines in its spectra, which are also similar to several SNe Ic-BL with high Ni-56 yields. The earliest spectrum shows 'flash ionization' features, from which we estimate a mass-loss rate of (M) over dot approximate to 2 x 10(-4 )M(circle dot) yr(-1). This wind density is too low to power the luminous light curve by ejecta-CSM interaction. We measure expansion velocities as high as 17 000 km s(-1) for H alpha, which is remarkably high compared to other SNe II. We estimate an oxygen core mass of 1.8-3.4 M-circle dot using the [O I] luminosity measured from a nebular-phase spectrum, implying a progenitor with a zero-age main-sequence mass of 19-26 M-circle dot.
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| 45. |
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| 46. |
- Brinck, Ingar, et al.
(författare)
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Dialogue in the making : Emotional engagement with materials
- 2020
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Ingår i: Phenomenology and the Cognitive Sciences. - : Springer Science and Business Media LLC. - 1572-8676 .- 1568-7759. ; 19:1, s. 23-45
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Tidskriftsartikel (refereegranskat)abstract
- Taking a psychological and philosophical outlook, we approach making as an embodied and embedded skill via the skilled artisan’s experience of having a corporeal, nonlinguistic dialogue with the material while working with it. We investigate the dynamic relation between maker and material through the lens of pottery as illustrated by wheel throwing, claiming that the experience of dialogue signals an emotional involvement with clay. The examination of Merleau-Ponty’s phenomenology of habit, the skilled intentionality framework, and material engagement theory show that while these theories explain complementary aspects of skilful engagement with the material world, they do not consider the dialogic dimension. By way of explanation, we submit that the artisan’s emotional engagement with the material world is based in openness and recognition and involves dialogue with the material. Drawing on the intimate relationship between movement and emotion, it promotes an open-ended manner of working and permits experiencing with the material, acting into its inherent possibilities. In conclusion, we suggest that dialogue, whether verbal or nonverbal, constitutes a primary means for making sense of the world at large, animate and inanimate.
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| 47. |
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| 48. |
- Bruin, WB, et al.
(författare)
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Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters
- 2020
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Ingår i: Translational psychiatry. - : Springer Science and Business Media LLC. - 2158-3188. ; 10:1, s. 342-
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Tidskriftsartikel (refereegranskat)abstract
- No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker.
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| 49. |
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| 50. |
- Caretta, Martina Angela, et al.
(författare)
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Migration as adaptation to freshwater and inland hydroclimatic changes? A meta-review of existing evidence
- 2023
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Ingår i: Climatic Change. - 0165-0009. ; 176:8
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Forskningsöversikt (refereegranskat)abstract
- Due to its potential geo-political and environmental implications, climate migration is an increasing concern to the international community. However, while there is considerable attention devoted to migration in response to sea-level rise, there is a limited understanding of human mobility due to freshwater and inland hydroclimatic changes. Hence, the aim of this paper is to examine the existing evidence on migration as an adaptation strategy due to freshwater and inland hydroclimatic changes. A meta-review of papers published between 2014 and 2019 yielded 67 publications, the majority of which focus on a handful of countries in the Global South. Droughts, floods, extreme heat, and changes in seasonal precipitation patterns were singled out as the most common hazards triggering migration. Importantly, most of the papers discuss mobility as part of a portfolio of responses. Motivations to migrate at the household level range from survival to searching for better economic opportunities. The outcomes of migration are mixed — spanning from higher incomes to difficulties in finding employment after moving and struggles with a higher cost of living. While remittances can be beneficial, migration does not always have a positive outcome for those who are left behind. Furthermore, this meta-review shows that migration, even when desired, is not an option for some of the most vulnerable households. These multifaceted results suggest that, while climate mobility is certainly happening due to freshwater and inland hydroclimatic changes, studies reviewing it are limited and substantial gaps remain in terms of geographical coverage, implementation assessments, and outcomes evaluation. We argue that these gaps need to be filled to inform climate and migration policies that increasingly need to be intertwined rather than shaped in isolation from each other.
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