| 1. |
- Borah, Jintu, et al.
(författare)
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AiCareBreath : IoT Enabled Location Invariant Novel Unified Model for Predicting Air Pollutants to Avoid Related Respiratory Disease
- 2024
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 11:8, s. 14625-14633
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Tidskriftsartikel (refereegranskat)abstract
- This article presents a location-invariant air pollution prediction model with good geographic generalizability. The model uses a Light GBR as part of a machine-learning framework to capture the spatial identification of air contaminants. Given the dynamic nature of air pollution, the model also uses a Random Forest to capture temporal dependencies in the data. Our model uses a transfer learning strategy to deal with location variability. The algorithm can learn concentration patterns because it has been trained on a vast dataset of air quality measurements from various locations. The trained model is then improved using information from a particular target site, customizing it to the features of the target area. Experiments are carried out on a comprehensive dataset containing air pollution measurements from various places to assess the efficacy of the proposed model. The recommended method performs better than standard models at forecasting air pollution levels, proving its dependability in various geographical settings. An interpretability analysis is also performed to learn about the variables affecting air pollution levels. We identify the geographical patterns associated with high pollutant concentrations by visualizing the learned representations within the model, giving important information for environmental planning and mitigation methods. The observations show that the model outperforms state-of-the-art forecasting based on RNNs and transformer-based models. The suggested methodology for forecasting air contaminants has the potential to improve air quality management and aid in decision-making across numerous regions. This helps safeguard the environment and public health by creating more precise and dependable air pollution forecast systems.
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| 2. |
- Ali, Sharib, et al.
(författare)
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Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
- 2024
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Ingår i: SCIENTIFIC REPORTS. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.
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| 3. |
- Anand, Shreya, et al.
(författare)
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Collapsars as Sites of r-process Nucleosynthesis : Systematic Photometric Near-infrared Follow-up of Type Ic-BL Supernovae
- 2024
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Ingår i: Astrophysical Journal. - : American Astronomical Society. - 0004-637X .- 1538-4357. ; 962:1
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Tidskriftsartikel (refereegranskat)abstract
- One of the open questions following the discovery of GW170817 is whether neutron star (NS) mergers are the only astrophysical sites capable of producing r-process elements. Simulations have shown that 0.01–0.1 M⊙ of r-process material could be generated in the outflows originating from the accretion disk surrounding the rapidly rotating black hole that forms as a remnant to both NS mergers and collapsing massive stars associated with long-duration gamma-ray bursts (collapsars). The hallmark signature of r-process nucleosynthesis in the binary NS merger GW170817 was its long-lasting near-infrared (NIR) emission, thus motivating a systematic photometric study of the light curves of broad-lined stripped-envelope (Ic-BL) supernovae (SNe) associated with collapsars. We present the first systematic study of 25 SNe Ic-BL—including 18 observed with the Zwicky Transient Facility and 7 from the literature—in the optical/NIR bands to determine what quantity of r-process material, if any, is synthesized in these explosions. Using semi-analytic models designed to account for r-process production in SNe Ic-BL, we perform light curve fitting to derive constraints on the r-process mass for these SNe. We also perform independent light curve fits to models without the r-process. We find that the r-process-free models are a better fit to the light curves of the objects in our sample. Thus, we find no compelling evidence of r-process enrichment in any of our objects. Further high-cadence infrared photometric studies and nebular spectroscopic analysis would be sensitive to smaller quantities of r-process ejecta mass or indicate whether all collapsars are completely devoid of r-process nucleosynthesis.
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| 4. |
- Kumar, Rahul, et al.
(författare)
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Bioinspired and Multifunctional Tribological Materials for Sliding, Erosive, Machining, and Energy-Absorbing Conditions: A Review
- 2024
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Ingår i: Biomimetics. - 2313-7673. ; 9:4
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Forskningsöversikt (refereegranskat)abstract
- Friction, wear, and the consequent energy dissipation pose significant challenges in systems with moving components, spanning various domains, including nanoelectromechanical systems (NEMS/MEMS) and bio-MEMS (microrobots), hip prostheses (biomaterials), offshore wind and hydro turbines, space vehicles, solar mirrors for photovoltaics, triboelectric generators, etc. Nature-inspired bionic surfaces offer valuable examples of effective texturing strategies, encompassing various geometric and topological approaches tailored to mitigate frictional effects and related functionalities in various scenarios. By employing biomimetic surface modifications, for example, roughness tailoring, multifunctionality of the system can be generated to efficiently reduce friction and wear, enhance load-bearing capacity, improve self-adaptiveness in different environments, improve chemical interactions, facilitate biological interactions, etc. However, the full potential of bioinspired texturing remains untapped due to the limited mechanistic understanding of functional aspects in tribological/biotribological settings. The current review extends to surface engineering and provides a comprehensive and critical assessment of bioinspired texturing that exhibits sustainable synergy between tribology and biology. The successful evolving examples from nature for surface/tribological solutions that can efficiently solve complex tribological problems in both dry and lubricated contact situations are comprehensively discussed. The review encompasses four major wear conditions: sliding, solid-particle erosion, machining or cutting, and impact (energy absorbing). Furthermore, it explores how topographies and their design parameters can provide tailored responses (multifunctionality) under specified tribological conditions. Additionally, an interdisciplinary perspective on the future potential of bioinspired materials and structures with enhanced wear resistance is presented.
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