| 1. |
- Khan, Sabih Ahmad, et al.
(författare)
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Investigation of the mechanical behavior of FDM processed CFRP/Al hybrid joint at elevated temperatures
- 2023
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Ingår i: Thin-walled structures. - : Elsevier BV. - 0263-8231 .- 1879-3223. ; 192
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Tidskriftsartikel (refereegranskat)abstract
- This research is focused on investigating the mechanical behavior of Fused Deposition Modeling (FDM) processed CFRP/Al hybrid riveted joints at elevated temperatures. A two-pronged approach was adopted entailing experimental and computational domains. In the experimental thrust, the developed joint was evaluated for its mechanical behavior by employing Digital Image Correlation, micro-XCT, and fractographic analysis. The tensile testing was performed at four different temperatures, i.e., Room Temperature (RT), 50°C, 75°C, and 100 °C. At RT, the joint experienced net-sectioning in the CFRP sheet along with minute secondary bending. Further, distinct failure modes were noticed for each ply orientation where the inherent porosity/voids appeared as the governing factor for the damage progression. Novel constitutive models were developed using accrued strain and change in energy dissipation to estimate the damage progression. The damage accumulation was found to be more uniform in the 0° layer as compared to 90°. Moreover, the 90° layer exhibited a more catastrophic damage pattern toward final failure. At elevated temperatures, a significant reduction in mechanical properties along with a non-uniform warping/bending of the plies was noticed due to viscoelastic behavior change. The computational analysis, having a hierarchical approach, was performed for the validation of the experimental results, and both were found to be in good agreement.
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| 2. |
- Munir, Muhammad Akhtar, et al.
(författare)
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Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection
- 2023
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Ingår i: 2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR). - : IEEE COMPUTER SOC. - 9798350301298 - 9798350301304 ; , s. 11474-11483
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Konferensbidrag (refereegranskat)abstract
- Deep neural networks (DNNs) have enabled astounding progress in several vision-based problems. Despite showing high predictive accuracy, recently, several works have revealed that they tend to provide overconfident predictions and thus are poorly calibrated. The majority of the works addressing the miscalibration of DNNs fall under the scope of classification and consider only in-domain predictions. However, there is little to no progress in studying the calibration of DNN-based object detection models, which are central to many vision-based safety-critical applications. In this paper, inspired by the train-time calibration methods, we propose a novel auxiliary loss formulation that explicitly aims to align the class confidence of bounding boxes with the accurateness of predictions (i.e. precision). Since the original formulation of our loss depends on the counts of true positives and false positives in a mini-batch, we develop a differentiable proxy of our loss that can be used during training with other application-specific loss functions. We perform extensive experiments on challenging in-domain and out-domain scenarios with six benchmark datasets including MS-COCO, Cityscapes, Sim10k, and BDD100k. Our results reveal that our train-time loss surpasses strong calibration baselines in reducing calibration error for both in and out-domain scenarios. Our source code and pre-trained models are available at https://github.com/akhtarvision/bpc_calibration
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| 3. |
- Shamshad, Fahad, et al.
(författare)
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Transformers in medical imaging: A survey
- 2023
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Ingår i: Medical Image Analysis. - : ELSEVIER. - 1361-8415 .- 1361-8423. ; 88
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Tidskriftsartikel (refereegranskat)abstract
- Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as de facto operators. Capitalizing on these advances in computer vision, the medical imaging field has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. Inspired from this transition, in this survey, we attempt to provide a comprehensive review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues. Specifically, we survey the use of Transformers in medical image segmentation, detection, classification, restoration, synthesis, registration, clinical report generation, and other tasks. In particular, for each of these applications, we develop taxonomy, identify application-specific challenges as well as provide insights to solve them, and highlight recent trends. Further, we provide a critical discussion of the fields current state as a whole, including the identification of key challenges, open problems, and outlining promising future directions. We hope this survey will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of Transformer models in medical imaging. Finally, to cope with the rapid development in this field, we intend to regularly update the relevant latest papers and their open-source implementations at https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging.
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| 4. |
- Iqbal, Sajid, et al.
(författare)
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Essential oils of four wild plants inhibit the blood seeking behaviour of female Aedes aegytpi
- 2023
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Ingår i: Experimental parasitology. - : Elsevier BV. - 0014-4894 .- 1090-2449. ; 244
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Tidskriftsartikel (refereegranskat)abstract
- Aedes aegypti (Diptera: Culicidae) mosquito is an important vector of many disease-causing pathogens. An effective way to escape from these mosquito-borne diseases is to prevent mosquito bites. In the current study, essential oils of Lepidium pinnatifidum, Mentha longifolia, Origanum vulgare, and Agrimonia eupatoria were evaluated for their repellent potential against Ae. aegypti females. Essential oils were extracted using steam distillation from freshly collected aerial parts of the plants and tested against 4–5 day old females of Ae. aegypti through the human bait technique for repellency and repellent longevity assays. The chemical composition of extracted essential oils was explored by gas chromatography coupled with mass spectrometry (GC-MS). The essential oils of L. pinnatifidum, M. longifolia, O. vulgare, and A. eupatoria at a dose of 33 μg/cm2 showed 100%, 94%, 87%, and 83% mosquito repellent activity, respectively. Furthermore, M. longifolia and O. vulgare essential oils exhibited 100% repellency at a dose of 165 μg/cm2, whereas A. eupatoria essential oil showed 100% repellency only at 330 μg/cm2. In the time-span bioassay, M. longifolia and O. vulgare essential oils showed protection against Ae. aegypti bites for 90 and 75 min, respectively whereas both A. eupatoria and L. pinnatifidum were found active for 45 min. Phenylacetonitrile (94%), piperitone oxide (34%), carvacrol (20%) and α-pinene (62%) were the most abundant compounds in L. pinnatifidum, M. longifolia, O. vulgare and A. eupatoria essential oils, respectively. The current study demonstrates that M. longifolia and O. vulgare essential oils possess the potential to be used as an alternative to synthetic chemicals to protect humans from mosquito bites.
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| 5. |
- Javed, Sajid, et al.
(författare)
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Visual Object Tracking With Discriminative Filters and Siamese Networks: A Survey and Outlook
- 2023
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE COMPUTER SOC. - 0162-8828 .- 1939-3539. ; 45:5, s. 6552-6574
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Tidskriftsartikel (refereegranskat)abstract
- Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems. It entails estimating the trajectory of the target in an image sequence, given only its initial location, and segmentation, or its rough approximation in the form of a bounding box. Discriminative Correlation Filters (DCFs) and deep Siamese Networks (SNs) have emerged as dominating tracking paradigms, which have led to significant progress. Following the rapid evolution of visual object tracking in the last decade, this survey presents a systematic and thorough review of more than 90 DCFs and Siamese trackers, based on results in nine tracking benchmarks. First, we present the background theory of both the DCF and Siamese tracking core formulations. Then, we distinguish and comprehensively review the shared as well as specific open research challenges in both these tracking paradigms. Furthermore, we thoroughly analyze the performance of DCF and Siamese trackers on nine benchmarks, covering different experimental aspects of visual tracking: datasets, evaluation metrics, performance, and speed comparisons. We finish the survey by presenting recommendations and suggestions for distinguished open challenges based on our analysis.
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| 6. |
- Shah, Shahid, et al.
(författare)
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Assessment of health-related quality of life among patients with obesity, hypertension and type 2 diabetes mellitus and its relationship with multimorbidity
- 2023
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 18:8
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Tidskriftsartikel (refereegranskat)abstract
- Obesity, hypertension (HTN) and type 2 diabetes (T2D) are among the multifactorial disorders that occur at higher prevalence in a population. This study aims to assess the health-related quality of life (HRQoL) of patients with obesity, HTN and T2D individually and in the form of multimorbidity. A questionnaire-based cross-sectional study was conducted among the patients in 15 private clinics of Punjab, Pakistan. A stratified random sampling technique was used to collect the data from patients with obesity, HTN and T2D or their comorbidity. A total of 1350 patients responded by completing the questionnaire. The HRQoL of these patients was assessed using the EQ-5D-5L questionnaire (a standardized instrument for measuring generic health status). Statistical analysis was performed using chi-square test, Mann-Whitney U test, and Kruskal-Wallis test. Multivariate linear regression model was used to model the visual analogue scale (VAS) score. In total, 15% of patients had combined obesity, HTN and T2D; 16.5% had HTN and T2D; 13.5% had obesity and HTN and 12.8% had obesity and T2D. Only 15.8% of patients had obesity, 14.3% had HTN, and 12% had T2D. Mann Whitney-U test gave the statistically significant (p = <0.001) HRQoL VAS score55.1 (±23.2) of patients with the obesity. HRQoL VAS scores of patients with obesity were found to be higher when compared to patients with both T2D 49.8 (±15.4) and HTN 48.2 (±21). Diagnosis of one, two and three diseases showed significant results in VAS with all variables including gender (p = 0.004), educational level (p = <0.001), marital status (p<0.001), residence (p = <0.001), financial situation (p = <0.001) and monthly income (p = <0.001). The most frequently observed extremely problematic dimension was anxiety/ depression (47%) and the self-care (10%) was the least affected. Patient HRQoL is decreased by T2D, HTN, and obesity. The impact of these diseases coexisting is more detrimental to HRQoL.
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