24,876 results on '"Naseri A"'
Search Results
2. A Table Theorem for Surfaces with Odd Euler Characteristic
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Sadr, Ali Naseri
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Mathematics - Geometric Topology ,Mathematics - Differential Geometry - Abstract
We use the square peg problem for smooth curves to prove a generalized table Theorem for real valued functions on Riemannian surfaces with odd Euler characteristic. We then use this result to prove the table conjecture for even functions on the two sphere.
- Published
- 2024
3. Quantum Active Learning for Structural Determination of Doped Nanoparticles -- a Case Study of 4Al@Si$_{11}$
- Author
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Lourenço, Maicon Pierre, Naseri, Mosayeb, Herrera, Lizandra Barrios, Zadeh-Haghighi, Hadi, Gaur, Daya, Simon, Christoph, and Salahub, Dennis R.
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Quantum Physics ,Condensed Matter - Materials Science - Abstract
Active learning (AL) has been widely applied in chemistry and materials science. In this work we propose a quantum active learning (QAL) method for automatic structural determination of doped nanoparticles, where quantum machine learning (QML) models for regression are used iteratively to indicate new structures to be calculated by DFT or DFTB and this new data acquisition is used to retrain the QML models. The QAL method is implemented in the Quantum Machine Learning Software/Agent for Material Design and Discovery (QMLMaterial), whose aim is using an artificial agent (defined by QML regression algorithms) that chooses the next doped configuration to be calculated that has a higher probability of finding the optimum structure. The QAL uses a quantum Gaussian process with a fidelity quantum kernel as well as the projected quantum kernel and different quantum circuits. For comparison, classical AL was used with a classical Gaussian process with different classical kernels. The presented QAL method was applied in the structural determination of doped Si$_{11}$ with 4 Al (4Al@Si$_{11}$) and the results indicate the QAL method is able to find the optimum 4Al@Si$_{11}$ structure. The aim of this work is to present the QAL method -- formulated in a noise-free quantum computing framework -- for automatic structural determination of doped nanoparticles and materials defects., Comment: 22 pages,6 figures
- Published
- 2024
4. High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning
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Naseri, Mostafa, De Poorter, Eli, Moerman, Ingrid, Poor, H. Vincent, and Shahid, Adnan
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Electrical Engineering and Systems Science - Signal Processing - Abstract
Co-channel interference cancellation (CCI) is the process used to reduce interference from other signals using the same frequency channel, thereby enhancing the performance of wireless communication systems. An improvement to this approach is blind CCI, which reduces interference without relying on prior knowledge of the interfering signal characteristics. Recent work suggested using machine learning (ML) models for this purpose, but high-throughput ML solutions are still lacking, especially for edge devices with limited resources. This work explores the adaptation of U-Net Convolutional Neural Network models for high-throughput blind source separation. Our approach is established on architectural modifications, notably through quantization and the incorporation of depthwise separable convolution, to achieve a balance between computational efficiency and performance. Our results demonstrate that the proposed models achieve superior MSE scores when removing unknown interference sources from the signals while maintaining significantly lower computational complexity compared to baseline models. One of our proposed models is deeper and fully convolutional, while the other is shallower with a convolutional structure incorporating an LSTM. Depthwise separable convolution and quantization further reduce the memory footprint and computational demands, albeit with some performance trade-offs. Specifically, applying depthwise separable convolutions to the model with the LSTM results in only a 0.72% degradation in MSE score while reducing MACs by 58.66%. For the fully convolutional model, we observe a 0.63% improvement in MSE score with even 61.10% fewer MACs. Overall, our findings underscore the feasibility of using optimized machine-learning models for interference cancellation in devices with limited resources.
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- 2024
5. Deep Learning-Based Image Compression for Wireless Communications: Impacts on Reliability,Throughput, and Latency
- Author
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Naseri, Mostafa, Ashtari, Pooya, Seif, Mohamed, De Poorter, Eli, Poor, H. Vincent, and Shahid, Adnan
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Electrical Engineering and Systems Science - Signal Processing ,Computer Science - Machine Learning ,Computer Science - Networking and Internet Architecture - Abstract
In wireless communications, efficient image transmission must balance reliability, throughput, and latency, especially under dynamic channel conditions. This paper presents an adaptive and progressive pipeline for learned image compression (LIC)-based architectures tailored to such environments. We investigate two state-of-the-art learning-based models: the hyperprior model and Vector Quantized Generative Adversarial Network (VQGAN). The hyperprior model achieves superior compression performance through lossless compression in the bottleneck but is susceptible to bit errors, necessitating the use of error correction or retransmission mechanisms. In contrast, the VQGAN decoder demonstrates robust image reconstruction capabilities even in the absence of channel coding, enhancing reliability in challenging transmission scenarios. We propose progressive versions of both models, enabling partial image transmission and decoding under imperfect channel conditions. This progressive approach not only maintains image integrity under poor channel conditions but also significantly reduces latency by allowing immediate partial image availability. We evaluate our pipeline using the Kodak high-resolution image dataset under a Rayleigh fading wireless channel model simulating dynamic conditions. The results indicate that the progressive transmission framework enhances reliability and latency while maintaining or improving throughput compared to non-progressive counterparts across various Signal-to-Noise Ratio (SNR) levels. Specifically, the progressive-hyperprior model consistently outperforms others in latency metrics, particularly in the 99.9th percentile waiting time-a measure indicating the maximum waiting time experienced by 99.9% of transmission instances-across all SNRs, and achieves higher throughput in low SNR scenarios. where Adaptive WebP fails.
- Published
- 2024
6. Exploring Quantum Active Learning for Materials Design and Discovery
- Author
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Lourenço, Maicon Pierre, Zadeh-Haghighi, Hadi, Hostaš, Jiří, Naseri, Mosayeb, Gaur, Daya, Simon, Christoph, and Salahub, Dennis R.
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Quantum Physics ,Condensed Matter - Materials Science ,Physics - Atomic and Molecular Clusters ,Physics - Chemical Physics - Abstract
The meeting of artificial intelligence (AI) and quantum computing is already a reality; quantum machine learning (QML) promises the design of better regression models. In this work, we extend our previous studies of materials discovery using classical active learning (AL), which showed remarkable economy of data, to explore the use of quantum algorithms within the AL framework (QAL) as implemented in the MLChem4D and QMLMaterials codes. The proposed QAL uses quantum support vector regressor (QSVR) or a quantum Gaussian process regressor (QGPR) with various quantum kernels and different feature maps. Data sets include perovskite properties (piezoelectric coefficient, band gap, energy storage) and the structure optimization of a doped nanoparticle (3Al@Si11) chosen to compare with classical AL results. Our results revealed that the QAL method improved the searches in most cases, but not all, seemingly correlated with the roughness of the data. QAL has the potential of finding optimum solutions, within chemical space, in materials science and elsewhere in chemistry., Comment: 30 pages, 6 figures
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- 2024
7. Investigation on corrosion behavior of Cu–TiO2 nanocomposite synthesized by the use of SHS method
- Author
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Naseri Azari Golnaz, Taghizadeh Tabrizi Arvin, and Hossein Aghajani
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Mining engineering. Metallurgy ,TN1-997 - Abstract
Enhancing the copper-based composites with oxide nanoparticles attracts more attention and many routes are proposed. In this investigation, stoichiometric Cu–TiO2 nanocomposite is synthesized through the self-propagating high-temperature synthesis, and the effect of additional Cu contents of 10, 20, 30 wt% on mechanical and physical features of this composite was evaluated. To investigate the obtained properties of specimens, scanning electron microscopy, X-ray diffractometry (XRD), micro-hardness, electrochemical impedance spectrometry and polarization tests were carried out. XRD results show that the metastable phase is formed within the synthesis. With increasing the Cu content, the amount of this phase is reduced. The maximum obtained Vickers micro-hardness is 369 HV for the sample, which contents 20 wt% Cu. Results show that the optimum excess copper content is 20 wt%. Also, EIS results show that an interaction between CuxO–TiO2 is decisive in the corrosion behavior of samples. Keywords: Cu–TiO2, Self-propagating high-temperature synthesis, Micro-hardness, Corrosion resistance, Nanoparticles
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- 2019
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8. Exploring pathways to forming twin stars
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Naseri, Mahdi, Bozzola, Gabriele, and Paschalidis, Vasileios
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Astrophysics - High Energy Astrophysical Phenomena ,General Relativity and Quantum Cosmology - Abstract
A viable model for the dense matter equation of state above the nuclear saturation density includes a hadron-to-quark phase transition at densities relevant to compact objects. In this case, stable hybrid hadron-quark stars can arise. An even more interesting scenario is one where the hadron-to-quark phase transition results in the emergence of a third branch of stable compact objects (in addition to white dwarfs and neutron stars). Inherent to the presence of a third family of compact stars is the existence of twin stars - hybrid stars with the same mass as the corresponding neutron stars, but with smaller radii. Interestingly, the neutron star-twin star scenario is consistent with GW170817. If twin stars exist in nature, it raises a question about the mechanism that leads to their formation. Here, we explore gravitational collapse as a pathway to the formation of low-mass twin stars. We perform fully general relativistic simulations of the collapse of a stellar iron core, modeled as a cold degenerate gas, to investigate whether the end product is a neutron star or a twin star. Our simulations show that even with unrealistically large perturbations in the initial conditions, the core bounces well below the hadron-to-quark phase transition density, if the initial total rest mass is in the twin star range. Following cooling, these configurations produce neutron stars. We find that twin stars can potentially form due to mass loss, e.g., through winds, from a slightly more massive hybrid star that was initially produced in the collapse of a more massive core or if the maximum neutron star mass is below the Chandrasekhar mass limit. The challenge in producing twin stars in gravitational collapse, in conjunction with the fine-tuning required because of their narrow mass range, suggests the rarity of twin stars in nature., Comment: 23 pages, 19 figures; matches PRD version
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- 2024
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9. Generated Contents Enrichment
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Naseri, Mahdi, Qiu, Jiayan, and Wang, Zhou
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
In this paper, we investigate a novel artificial intelligence generation task termed Generated Contents Enrichment (GCE). Conventional AI content generation produces visually realistic content by implicitly enriching the given textual description based on limited semantic descriptions. Unlike this traditional task, our proposed GCE strives to perform content enrichment explicitly in both the visual and textual domains. The goal is to generate content that is visually realistic, structurally coherent, and semantically abundant. To tackle GCE, we propose a deep end-to-end adversarial method that explicitly explores semantics and inter-semantic relationships during the enrichment process. Our approach first models the input description as a scene graph, where nodes represent objects and edges capture inter-object relationships. We then adopt Graph Convolutional Networks on top of the input scene description to predict additional enriching objects and their relationships with the existing ones. Finally, the enriched description is passed to an image synthesis model to generate the corresponding visual content. Experiments conducted on the Visual Genome dataset demonstrate the effectiveness of our method, producing promising and visually plausible results.
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- 2024
10. Classification of Lattices Bounded by Large Surgeries of Knots
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Sadr, Ali Naseri
- Subjects
Mathematics - Geometric Topology - Abstract
We classify all the lattices realized as the intersection form of a positive definite four manifold with boundary $S_n^3(K)$ for a knot $K$ in the three sphere and a positive integer $n$ greater than $4g_4(K)+3$. We then use this result to define a concordance invariant and generalize a theorem of Rasmussen on lens space surgeries.
- Published
- 2024
11. Synchronization levels in EEG connectivity during cognitive workloads while driving: Synchronization levels in EEG connectivity during cognitive workloads while driving
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Naseri, Nafise, Parastesh, Fatemeh, Ghassemi, Farnaz, Jafari, Sajad, Perc, Matjaž, and Završnik, Jernej
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- 2024
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12. AI Chatbots in Oncology: A Comparative Study of Sider Fusion AI and Perplexity AI for Gastric Cancer Patients
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Naseri, Amirhosein, Antikchi, Mohammad Hossein, Barahman, Maedeh, Shirinzadeh-Dastgiri, Ahmad, HaghighiKian, Seyed Masoud, Vakili-Ojarood, Mohammad, Rahmani, Amirhossein, Shahbazi, Amirhossein, Shiri, Amirmasoud, Masoudi, Ali, Aghasipour, Maryam, Aghili, Kazem, and Neamatzadeh, Hossein
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- 2024
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13. Efficacy of Probiotics in Overweight and Obesity Control: An Umbrella Review and Subgroup Meta-Analysis
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Sadeghi, A., Daroudi, R., Davari, M., Gharib-Naseri, Z., Jafarzadeh, J., and Tajvar, M.
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- 2024
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14. Evaluation of Amino acid and Mineral Profiles in the Body Wall of Holothuria Leocospilota and Stichopus herrmanni in Chabahar Bay, Gulf of Oman
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Rahul, Shahab, Shakouri, Arash, and Naseri, Fatemeh
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- 2024
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15. Moldable Alginate/Hydroxyapatite Hydrogel Loaded with Metformin Enhanced Regeneration of the Rabbit Mandibular Defects
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Nabavizadeh, Sara S, Shadi, Mehri, Khorraminejad-Shirazi, Mohammadhossein, Daneshi, Sajad, Tavanafar, Saeid, Naseri, Reyhaneh, Abbaspour, Alireza, Alaei-Jahromi, Keivan, and Talaei-Khozani, Tahereh
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- 2024
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16. TIRADS-based artificial intelligence systems for ultrasound images of thyroid nodules: protocol for a systematic review
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Sharifi, Yasaman, Amiri Tehranizadeh, Amin, Danay Ashgzari, Morteza, and Naseri, Zeinab
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- 2024
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17. Ginger Extract Improves Cognitive Dysfunction via Modulation of Gut Microbiota-Derived Short-Chain Fatty Acids in D-Galactose/Ovariectomy-Induced Alzheimer-Like Disease
- Author
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Zahedi, Elham, Naseri, Fatemeh Mokhtari, Zamani, Elham, Nikbakhtzadeh, Marjan, Rastegar, Tayebeh, Sanaeirad, Ashkan, and Sadr, Seyed Shahabeddin
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- 2024
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18. Tethered cord prevalence among patients with prenatal or postnatal myelomeningocele repair
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Naseri Alavi, Seyed Ahmad, Fluss, Ross, Lehner, Kurt, Judy, Brendan, Groves, Mari, Cohen, Alan, and Kobets, Andrew J.
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- 2024
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19. Shaping childhood obesity: behavioral and environmental risk factors associated with body mass index trajectories between 2 and 9 years in Samoan children
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Choy, Courtney C., Johnson, William, Naseri, Take, Filipo, Vaimoana, Arorae, Maria Siulepa, Tafunaina, Faatali, Unasa, Folla, Savusa, Kima, Reupena, Muagututia S., Braun, Joseph M., Duckham, Rachel L., Soti-Ulberg, Christina, McGarvey, Stephen T., and Hawley, Nicola L.
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- 2024
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20. Predicting Immunohistochemical Biomarkers of Breast Cancer Using 18F-FDG PET/CT Radiomics: A Multicenter Study
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Faraji, Sahar, Emami, Farshad, Vosoughi, Zahra, Hajianfar, Ghasem, Naseri, Shahrokh, Samimi, Rezvan, Vosoughi, Habibeh, Geramifar, Parham, and Zaidi, Habib
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- 2024
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21. UAV-Based Detection of Deciduous Tree Species Using Structural and Spectral Characteristics
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Naseri, Mohammad Hassan and Shataee Jouibary, Shaban
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- 2024
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22. Shining a Light on Selenium: a Meta-analysis of Supplementation in Multiple Sclerosis
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Rahmani, Mehrab, Pakkhesal, Sina, Baharomid, Saman, Karimi, Hanie, Mosaddeghi-Heris, Reza, Talebi, Mahnaz, Aghaei, Negar, Rahimi-Mamaghani, Alireza, Sanaie, Sarvin, and Naseri, Amirreza
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- 2024
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23. Evaluation of intestinal damage biomarkers in calves with atresia coli
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Yildiz Ramazan, Ok Mahmut, Ider Merve, Aydogdu Ugur, Naseri Amir, Parlak Kurtulus, and Gulersoy Erdem
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calves ,atresia coli ,ifabp ,lfabp ,tff3 ,Veterinary medicine ,SF600-1100 - Abstract
Intestinal obstruction such as atresia coli causes pathophysiological changes in gastrointestinal tissue due to the rise of intra-abdominal pressure. The aim of this study is to determine the intestinal damage with intestinal biomarkers in calves with atresia coli.
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- 2018
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24. Coherence generation with Hamiltonians
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Scalici, Manfredi, Naseri, Moein, and Streltsov, Alexander
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Quantum Physics - Abstract
We explore methods to generate quantum coherence through unitary evolutions, by introducing and studying the coherence generating capacity of Hamiltonians. This quantity is defined as the maximum derivative of coherence that can be achieved by a Hamiltonian. By adopting the relative entropy of coherence as our figure of merit, we evaluate the maximal coherence generating capacity with the constraint of a bounded Hilbert-Schmidt norm for the Hamiltonian. Our investigation yields closed-form expressions for both Hamiltonians and quantum states that induce the maximal derivative of coherence under these conditions. Specifically, for qubit systems, we solve this problem comprehensively for any given Hamiltonian, identifying the quantum states that lead to the largest coherence derivative induced by the Hamiltonian. Our investigation enables a precise identification of conditions under which quantum coherence is optimally enhanced, offering valuable insights for the manipulation and control of quantum coherence in quantum systems., Comment: 7 pages
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- 2024
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25. Anomalous conductivity due to relativistic Landau quantization
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Brodin, Gert and Al-Naseri, Haidar
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Physics - Plasma Physics ,High Energy Physics - Phenomenology - Abstract
We use a recently developed kinetic model derived from the Dirac equation, in order to study electromagnetic wave propagation in superstrong magnetic fields, such as in magnetars, where relativistic Landau quantization is prominent. The leading contribution to the conductivity tensor in such a plasma is calculated. It is found that the electron Hall current has an anomalous contribution, in the quantum relativistic regime, where the effective particle energy has a significant contribution from the diamagnetic and Zeeman energy. As a result, a new quantum resonance frequency appears, and the dispersion relation for the left- and right-hand polarized modes are strongly modified for long and moderate wavelengths. The implications for magnetar physics are discussed.
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- 2024
26. ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning
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Naseri, Mahdi and Biparva, Mahdi
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Self-supervised Learning (SSL) has emerged as a powerful technique in pre-training deep learning models without relying on expensive annotated labels, instead leveraging embedded signals in unlabeled data. While SSL has shown remarkable success in computer vision tasks through intuitive data augmentation, its application to graph-structured data poses challenges due to the semantic-altering and counter-intuitive nature of graph augmentations. Addressing this limitation, this paper introduces a novel non-contrastive SSL approach to Explicitly Generate a compositional Relation Graph (ExGRG) instead of relying solely on the conventional augmentation-based implicit relation graph. ExGRG offers a framework for incorporating prior domain knowledge and online extracted information into the SSL invariance objective, drawing inspiration from the Laplacian Eigenmap and Expectation-Maximization (EM). Employing an EM perspective on SSL, our E-step involves relation graph generation to identify candidates to guide the SSL invariance objective, and M-step updates the model parameters by integrating the derived relational information. Extensive experimentation on diverse node classification datasets demonstrates the superiority of our method over state-of-the-art techniques, affirming ExGRG as an effective adoption of SSL for graph representation learning.
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- 2024
27. Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection
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Abadi, Aydin, Doyle, Bradley, Gini, Francesco, Guinamard, Kieron, Murakonda, Sasi Kumar, Liddell, Jack, Mellor, Paul, Murdoch, Steven J., Naseri, Mohammad, Page, Hector, Theodorakopoulos, George, and Weller, Suzanne
- Subjects
Computer Science - Machine Learning ,Computer Science - Cryptography and Security - Abstract
Federated Learning (FL) is a data-minimization approach enabling collaborative model training across diverse clients with local data, avoiding direct data exchange. However, state-of-the-art FL solutions to identify fraudulent financial transactions exhibit a subset of the following limitations. They (1) lack a formal security definition and proof, (2) assume prior freezing of suspicious customers' accounts by financial institutions (limiting the solutions' adoption), (3) scale poorly, involving either $O(n^2)$ computationally expensive modular exponentiation (where $n$ is the total number of financial institutions) or highly inefficient fully homomorphic encryption, (4) assume the parties have already completed the identity alignment phase, hence excluding it from the implementation, performance evaluation, and security analysis, and (5) struggle to resist clients' dropouts. This work introduces Starlit, a novel scalable privacy-preserving FL mechanism that overcomes these limitations. It has various applications, such as enhancing financial fraud detection, mitigating terrorism, and enhancing digital health. We implemented Starlit and conducted a thorough performance analysis using synthetic data from a key player in global financial transactions. The evaluation indicates Starlit's scalability, efficiency, and accuracy.
- Published
- 2024
28. A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer
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HaghighiKian, Seyed Masoud, Shirinzadeh-Dastgiri, Ahmad, Vakili-Ojarood, Mohammad, Naseri, Amirhosein, Barahman, Maedeh, Saberi, Ali, Rahmani, Amirhossein, Shiri, Amirmasoud, Masoudi, Ali, Aghasipour, Maryam, Shahbazi, Amirhossein, Ghelmani, Yaser, Aghili, Kazem, and Neamatzadeh, Hossein
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- 2024
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29. Ethical Considerations and Equipoise in Cancer Surgery
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Vakili-Ojarood, Mohammad, Naseri, Amirhosein, Shirinzadeh-Dastgiri, Ahmad, Saberi, Ali, HaghighiKian, Seyed Masoud, Rahmani, Amirhossein, Farnoush, Nazila, Nafissi, Nahid, Heiranizadeh, Naeimeh, Antikchi, Mohamad Hossein, Narimani, Nima, Atarod, Mohammad Mehdi, Yeganegi, Maryam, and Neamatzadeh, Hossein
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- 2024
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30. From a focal skin issue to a systemic disease: the multifaceted nature of cold sores, novel findings
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Akhoundi, Neda, Noorbakhsh, Mahta, Siami, Alireza, Naseri, Zahra, Hazara, Sara, and Hazara, Kobra
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- 2024
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31. Types of Short-Duration Electrical Stimulation-Induced Efficiency in the Axonal Regeneration and Recovery: Comparative in Vivo Study in Rat Model of Repaired Sciatic Nerve and its Tibial Branch after Transection Injury
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Naseri, Sareh, Samaram, Hosein, Naghavi, Nadia, Rassouli, Morteza Behnam, and Mousavinezhad, Maryam
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- 2024
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32. Prognostic risk factors for early outcomes of patients with myelomeningocele: a prospective study
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Naseri Alavi, Seyed Ahmad, Rezkhah, Amir, Majdi, Alireza, Habibi, Mohammad Amin, Bagheri, Mohammad Mehdi, Jafarzadeh, Fateme, and Kobets, Andrew J.
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- 2024
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33. An agent-based modelling framework for performance assessment of search and rescue operations in the Barents Sea
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Ashrafi, Behrooz, Kim, Gibeom, Naseri, Masoud, Barabady, Javad, Dhar, Sushmit, Heo, Gyunyoung, and Baek, Sejin
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- 2024
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34. Which variables influence electric vehicle adoption?
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Naseri, Hamed, Waygood, E. O. D., Patterson, Zachary, and Wang, Bobin
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- 2024
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35. Mechanical Properties of the Al3(TiTaZrNbHf) High Entropy Intermetallic Compound: A Molecular Dynamic Study
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Moghaddam, A. Ostovari, Fereidonnejad, R., Mikhailov, D. V., Naseri, M., and Trofimov, E. A.
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- 2024
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36. Assessing climate-induced risks to urban railway infrastructure
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Garmabaki, A. H. S., Naseri, Masoud, Odelius, Johan, Famurewa, Stephen, Asplund, Matthias, and Strandberg, Gustav
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- 2024
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37. Synthesis, characterization, and adsorption capacity of sodium alginate poly grafted (fumaric acid-co-polyacrylic acid)/graphene oxide hydrogel as adsorbent for Cr (VI) and Pb (II) removal
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Alnasery, Hiba, Naseri, Abdolhossein, Jasim, Layth Samir, and Sajedi-Amin, Sanaz
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- 2024
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38. Clear cell meningiomas—case presentation, review of radiographic identifiers, and treatment approaches
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Keymakh, Margaret, Benton, Joshua A., Fluss, Rose, Alavi, Seyed Ahmad Naseri, Martin, Allison M., Chin, Steven, and Kobets, Andrew J.
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- 2024
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39. Investigating the role of path architecture complexity in users’ movement patterns in hospital circulation systems: case studies in Golestan, Iran
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Saeidi, Maryam, Siyahkali, Mahsa Delshad, Moradinasab, Hossein, and Naseri, Gholamhossein
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- 2024
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40. A novel approach to study laser induced void array formation in fused silica
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Naseri, N., Crenshaw, M., Samarbakhsh, A., and Ramunno, L.
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Physics - Optics ,Physics - Applied Physics - Abstract
This study investigated multi-void formation in fused silica using high resolution Finite-Difference-Time-Domain (FDTD) simulations. Despite extensive research dedicated to understanding the mechanisms behind multi-void formation in materials, the fundamental aspects and mechanisms governing self-void array formation in dielectrics and polymers remain poorly understood. By modeling the voids as concentric spheres with densified shells and simulating the laser interaction with the voids, we showed that void array generation in fused silica is a linear mechanism. This study provides valuable insight into the mechanism behind the formation of void arrays in fused silica., Comment: arXiv admin note: substantial text overlap with arXiv:2305.02976
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- 2023
41. Improving performance of heart rate time series classification by grouping subjects
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Beekhuizen, Michael, Naseri, Arman, Tax, David, van der Bilt, Ivo, and Reinders, Marcel
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Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Signal Processing - Abstract
Unlike the more commonly analyzed ECG or PPG data for activity classification, heart rate time series data is less detailed, often noisier and can contain missing data points. Using the BigIdeasLab_STEP dataset, which includes heart rate time series annotated with specific tasks performed by individuals, we sought to determine if general classification was achievable. Our analyses showed that the accuracy is sensitive to the choice of window/stride size. Moreover, we found variable classification performances between subjects due to differences in the physical structure of their hearts. Various techniques were used to minimize this variability. First of all, normalization proved to be a crucial step and significantly improved the performance. Secondly, grouping subjects and performing classification inside a group helped to improve performance and decrease inter-subject variability. Finally, we show that including handcrafted features as input to a deep learning (DL) network improves the classification performance further. Together, these findings indicate that heart rate time series can be utilized for classification tasks like predicting activity. However, normalization or grouping techniques need to be chosen carefully to minimize the issue of subject variability.
- Published
- 2023
42. Local Purity Distillation in Quantum Systems: Exploring the Complementarity Between Purity and Entanglement
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Ganardi, Ray, Masajada, Piotr, Naseri, Moein, and Streltsov, Alexander
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Quantum Physics - Abstract
Quantum thermodynamics and quantum entanglement represent two pivotal quantum resource theories with significant relevance in quantum information science. Despite their importance, the intricate relationship between these two theories is still not fully understood. Here, we delve into the interplay between entanglement and thermodynamics, particularly in the context of local cooling processes. We introduce and develop the framework of Gibbs-preserving local operations and classical communication. Within this framework, we explore strategies enabling remote parties to effectively cool their local systems to the ground state. Our analysis is centered on scenarios where only a single copy of a quantum state is accessible, with the ideal performance defined by the highest possible fidelity to the ground state achievable under these constraints. We focus on systems with fully degenerate local Hamiltonians, where local cooling aligns with the extraction of local purity. In this context, we establish a powerful link between the efficiency of local purity extraction and the degree of entanglement present in the system, a concept we define as purity-entanglement complementarity. Moreover, we demonstrate that in many pertinent scenarios, the optimal performance can be precisely determined through semidefinite programming techniques. Our findings open doors to various practical applications, including techniques for entanglement detection and estimation. We demonstrate this by evaluating the amount of entanglement for a class of bound entangled states., Comment: 5+5 pages, 4 figures
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- 2023
43. Pushing the Limits of Quantum Computing for Simulating PFAS Chemistry
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Dimitrov, Emil, Sanchez-Sanz, Goar, Nelson, James, O'Riordan, Lee, Doyle, Myles, Courtney, Sean, Kannan, Venkatesh, Naseri, Hassan, Garcia, Alberto Garcia, Tricker, James, Faraggi, Marisa, Goings, Joshua, and Zhao, Luning
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Quantum Physics ,Computer Science - Computational Engineering, Finance, and Science - Abstract
Accurate and scalable methods for computational quantum chemistry can accelerate research and development in many fields, ranging from drug discovery to advanced material design. Solving the electronic Schrodinger equation is the core problem of computational chemistry. However, the combinatorial complexity of this problem makes it intractable to find exact solutions, except for very small systems. The idea of quantum computing originated from this computational challenge in simulating quantum-mechanics. We propose an end-to-end quantum chemistry pipeline based on the variational quantum eigensolver (VQE) algorithm and integrated with both HPC-based simulators and a trapped-ion quantum computer. Our platform orchestrates hundreds of simulation jobs on compute resources to efficiently complete a set of ab initio chemistry experiments with a wide range of parameterization. Per- and poly-fluoroalkyl substances (PFAS) are a large family of human-made chemicals that pose a major environmental and health issue globally. Our simulations includes breaking a Carbon-Fluorine bond in trifluoroacetic acid (TFA), a common PFAS chemical. This is a common pathway towards destruction and removal of PFAS. Molecules are modeled on both a quantum simulator and a trapped-ion quantum computer, specifically IonQ Aria. Using basic error mitigation techniques, the 11-qubit TFA model (56 entangling gates) on IonQ Aria yields near-quantitative results with milli-Hartree accuracy. Our novel results show the current state and future projections for quantum computing in solving the electronic structure problem, push the boundaries for the VQE algorithm and quantum computers, and facilitates development of quantum chemistry workflows.
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- 2023
44. Validation of human telomere length multi-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as human telomere length regulation genes
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Keener, Rebecca, Chhetri, Surya B, Connelly, Carla J, Taub, Margaret A, Conomos, Matthew P, Weinstock, Joshua, Ni, Bohan, Strober, Benjamin, Aslibekyan, Stella, Auer, Paul L, Barwick, Lucas, Becker, Lewis C, Blangero, John, Bleecker, Eugene R, Brody, Jennifer A, Cade, Brian E, Celedon, Juan C, Chang, Yi-Cheng, Cupples, L Adrienne, Custer, Brian, Freedman, Barry I, Gladwin, Mark T, Heckbert, Susan R, Hou, Lifang, Irvin, Marguerite R, Isasi, Carmen R, Johnsen, Jill M, Kenny, Eimear E, Kooperberg, Charles, Minster, Ryan L, Naseri, Take, Viali, Satupa’itea, Nekhai, Sergei, Pankratz, Nathan, Peyser, Patricia A, Taylor, Kent D, Telen, Marilyn J, Wu, Baojun, Yanek, Lisa R, Yang, Ivana V, Albert, Christine, Arnett, Donna K, Ashley-Koch, Allison E, Barnes, Kathleen C, Bis, Joshua C, Blackwell, Thomas W, Boerwinkle, Eric, Burchard, Esteban G, Carson, April P, Chen, Zhanghua, Chen, Yii-Der Ida, Darbar, Dawood, de Andrade, Mariza, Ellinor, Patrick T, Fornage, Myriam, Gelb, Bruce D, Gilliland, Frank D, He, Jiang, Islam, Talat, Kaab, Stefan, Kardia, Sharon LR, Kelly, Shannon, Konkle, Barbara A, Kumar, Rajesh, Loos, Ruth JF, Martinez, Fernando D, McGarvey, Stephen T, Meyers, Deborah A, Mitchell, Braxton D, Montgomery, Courtney G, North, Kari E, Palmer, Nicholette D, Peralta, Juan M, Raby, Benjamin A, Redline, Susan, Rich, Stephen S, Roden, Dan, Rotter, Jerome I, Ruczinski, Ingo, Schwartz, David, Sciurba, Frank, Shoemaker, M Benjamin, Silverman, Edwin K, Sinner, Moritz F, Smith, Nicholas L, Smith, Albert V, Tiwari, Hemant K, Vasan, Ramachandran S, Weiss, Scott T, Williams, L Keoki, Zhang, Yingze, Ziv, Elad, Raffield, Laura M, Reiner, Alexander P, Arvanitis, Marios, Greider, Carol W, Mathias, Rasika A, and Battle, Alexis
- Subjects
Biological Sciences ,Genetics ,Human Genome ,1.1 Normal biological development and functioning ,2.1 Biological and endogenous factors ,Generic health relevance ,Humans ,Genome-Wide Association Study ,Telomere ,K562 Cells ,Telomere Homeostasis ,Polymorphism ,Single Nucleotide ,Gene Expression Regulation ,CRISPR-Cas Systems ,NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium ,TOPMed Hematology and Hemostasis Working Group ,TOPMed Structural Variation Working Group - Abstract
Genome-wide association studies (GWAS) have become well-powered to detect loci associated with telomere length. However, no prior work has validated genes nominated by GWAS to examine their role in telomere length regulation. We conducted a multi-ancestry meta-analysis of 211,369 individuals and identified five novel association signals. Enrichment analyses of chromatin state and cell-type heritability suggested that blood/immune cells are the most relevant cell type to examine telomere length association signals. We validated specific GWAS associations by overexpressing KBTBD6 or POP5 and demonstrated that both lengthened telomeres. CRISPR/Cas9 deletion of the predicted causal regions in K562 blood cells reduced expression of these genes, demonstrating that these loci are related to transcriptional regulation of KBTBD6 and POP5. Our results demonstrate the utility of telomere length GWAS in the identification of telomere length regulation mechanisms and validate KBTBD6 and POP5 as genes affecting telomere length regulation.
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- 2024
45. Synthesis and characterization of metal dithiocarbamate derivatives of (S)-1-phenyl-1,2,3,4-tetrahydroisoquinoline
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Halimehjani, Azim Ziyaei, Amani, Vahid, Naseri, Fatemeh, and Notash, Behrouz
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- 2024
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46. Highly efficient electrochemical biosensing platform in breast cancer detection based on MOF-COF@Au core-shell like nanostructure
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Dezhakam, Ehsan, Vayghan, Roya Faraghi, Dehghani, Sarina, Kafili-Hajlari, Taha, Naseri, Abdolhossein, Dadashpour, Mehdi, Khalilzadeh, Balal, and Kanberoglu, Gulsah Saydan
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- 2024
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47. Dry needling as an adjunct treatment to multimodal rehabilitation protocol following rotator cuff repair surgery: a preliminary, randomized sham-controlled trial
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Naseri, Faeze, Dadgoo, Mehdi, Pourahmadi, Mohammadreza, Amroodi, Morteza Nakhaei, Azizi, Shirin, and Shamsi, Amirhossein
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- 2024
- Full Text
- View/download PDF
48. Using pile-up collisions as an abundant source of low-energy hadronic physics processes in ATLAS and an extraction of the jet energy resolution
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Aad, G., Aakvaag, E., Abbott, B., Abdelhameed, S., Abeling, K., Abicht, N. J., Abidi, S. H., Aboelela, M., Aboulhorma, A., Abramowicz, H., Abreu, H., Abulaiti, Y., Acharya, B. S., Ackermann, A., Adam Bourdarios, C., Adamczyk, L., Addepalli, S. V., Addison, M. J., Adelman, J., Adiguzel, A., Adye, T., Affolder, A. A., Afik, Y., Agaras, M. N., Agarwala, J., Aggarwal, A., Agheorghiesei, C., Ahmadov, F., Ahmed, W. S., Ahuja, S., Ai, X., Aielli, G., Aikot, A., Ait Tamlihat, M., Aitbenchikh, B., Akbiyik, M., Åkesson, T. P. A., Akimov, A. V., Akiyama, D., Akolkar, N. N., Aktas, S., Al Khoury, K., Alberghi, G. L., Albert, J., Albicocco, P., Albouy, G. L., Alderweireldt, S., Alegria, Z. L., Aleksa, M., Aleksandrov, I. N., Alexa, C., Alexopoulos, T., Alfonsi, F., Algren, M., Alhroob, M., Ali, B., Ali, H. M. J., Ali, S., Alibocus, S. W., Aliev, M., Alimonti, G., Alkakhi, W., Allaire, C., Allbrooke, B. M. M., Allen, J. S., Allen, J. F., Allendes Flores, C. A., Allport, P. P., Aloisio, A., Alonso, F., Alpigiani, C., Alsolami, Z. M. K., Alvarez Estevez, M., Alvarez Fernandez, A., Alves Cardoso, M., Alviggi, M. G., Aly, M., Amaral Coutinho, Y., Ambler, A., Amelung, C., Amerl, M., Ames, C. G., Amidei, D., Amini, B., Amirie, K. J., Amor Dos Santos, S. P., Amos, K. R., Amperiadou, D., An, S., Ananiev, V., Anastopoulos, C., Andeen, T., Anders, J. K., Anderson, A. C., Andrean, S. Y., Andreazza, A., Angelidakis, S., Angerami, A., Anisenkov, A. V., Annovi, A., Antel, C., Antipov, E., Antonelli, M., Anulli, F., Aoki, M., Aoki, T., Aparo, M. A., Aperio Bella, L., Appelt, C., Apyan, A., Arbiol Val, S. J., Arcangeletti, C., Arce, A. T. H., Arguin, J-F., Argyropoulos, S., Arling, J.-H., Arnaez, O., Arnold, H., Artoni, G., Asada, H., Asai, K., Asai, S., Asbah, N. A., Ashby Pickering, R. A., Assamagan, K., Astalos, R., Astrand, K. S. V., Atashi, S., Atkin, R. J., Atkinson, M., Atmani, H., Atmasiddha, P. A., Augsten, K., Auricchio, S., Auriol, A. D., Austrup, V. A., Avolio, G., Axiotis, K., Azuelos, G., Babal, D., Bachacou, H., Bachas, K., Bachiu, A., Bachmann, E., Backman, F., Badea, A., Baer, T. M., Bagnaia, P., Bahmani, M., Bahner, D., Bai, K., Baines, J. T., Baines, L., Baker, O. K., Bakos, E., Bakshi Gupta, D., Balabram Filho, L. E., Balakrishnan, V., Balasubramanian, R., Baldin, E. M., Balek, P., Ballabene, E., Balli, F., Baltes, L. M., Balunas, W. K., Balz, J., Bamwidhi, I., Banas, E., Bandieramonte, M., Bandyopadhyay, A., Bansal, S., Barak, L., Barakat, M., Barberio, E. L., Barberis, D., Barbero, M., Barel, M. Z., Barillari, T., Barisits, M-S., Barklow, T., Baron, P., Baron Moreno, D. A., Baroncelli, A., Barr, A. J., Barr, J. D., Barreiro, F., Barreiro Guimarães da Costa, J., Barron, U., Barros Teixeira, M. G., Barsov, S., Bartels, F., Bartoldus, R., Barton, A. E., Bartos, P., Basan, A., Baselga, M., Bassalat, A., Basso, M. J., Bataju, S., Bate, R., Bates, R. L., Batlamous, S., Batool, B., Battaglia, M., Battulga, D., Bauce, M., Bauer, M., Bauer, P., Bazzano Hurrell, L. T., Beacham, J. B., Beau, T., Beaucamp, J. Y., Beauchemin, P. H., Bechtle, P., Beck, H. P., Becker, K., Beddall, A. J., Bednyakov, V. A., Bee, C. P., Beemster, L. J., Beermann, T. A., Begalli, M., Begel, M., Behera, A., Behr, J. K., Beirer, J. F., Beisiegel, F., Belfkir, M., Bella, G., Bellagamba, L., Bellerive, A., Bellos, P., Beloborodov, K., Benchekroun, D., Bendebba, F., Benhammou, Y., Benkendorfer, K. C., Beresford, L., Beretta, M., Bergeaas Kuutmann, E., Berger, N., Bergmann, B., Beringer, J., Bernardi, G., Bernius, C., Bernlochner, F. U., Bernon, F., Berrocal Guardia, A., Berry, T., Berta, P., Berthold, A., Bethke, S., Betti, A., Bevan, A. J., Bhalla, N. K., Bhatta, S., Bhattacharya, D. S., Bhattarai, P., Bhide, K. D., Bhopatkar, V. S., Bianchi, R. M., Bianco, G., Biebel, O., Bielski, R., Biglietti, M., Billingsley, C. S., Bimgdi, Y., Bindi, M., Bingul, A., Bini, C., Bird, G. A., Birman, M., Biros, M., Biryukov, S., Bisanz, T., Bisceglie, E., Biswal, J. P., Biswas, D., Bloch, I., Blue, A., Blumenschein, U., Blumenthal, J., Bobrovnikov, V. S., Boehler, M., Boehm, B., Bogavac, D., Bogdanchikov, A. G., Boggia, L. S., Bohm, C., Boisvert, V., Bokan, P., Bold, T., Bomben, M., Bona, M., Boonekamp, M., Booth, C. D., Borbély, A. G., Bordulev, I. S., Borissov, G., Bortoletto, D., Boscherini, D., Bosman, M., Bossio Sola, J. D., Bouaouda, K., Bouchhar, N., Boudet, L., Boudreau, J., Bouhova-Thacker, E. V., Boumediene, D., Bouquet, R., Boveia, A., Boyd, J., Boye, D., Boyko, I. R., Bozianu, L., Bracinik, J., Brahimi, N., Brandt, G., Brandt, O., Braren, F., Brau, B., Brau, J. E., Brener, R., Brenner, L., Brenner, R., Bressler, S., Brianti, G., Britton, D., Britzger, D., Brock, I., Brock, R., Brooijmans, G., Brooks, E. M., Brost, E., Brown, L. M., Bruce, L. E., Bruckler, T. L., Bruckman de Renstrom, P. A., Brüers, B., Bruni, A., Bruni, G., Bruschi, M., Bruscino, N., Buanes, T., Buat, Q., Buchin, D., Buckley, A. G., Bulekov, O., Bullard, B. A., Burdin, S., Burgard, C. D., Burger, A. M., Burghgrave, B., Burlayenko, O., Burleson, J., Burr, J. T. P., Burzynski, J. C., Busch, E. L., Büscher, V., Bussey, P. J., Butler, J. M., Buttar, C. M., Butterworth, J. M., Buttinger, W., Buxo Vazquez, C. J., Buzykaev, A. R., Cabrera Urbán, S., Cadamuro, L., Caforio, D., Cai, H., Cai, Y., Cai, Y., Cairo, V. M. M., Cakir, O., Calace, N., Calafiura, P., Calderini, G., Calfayan, P., Callea, G., Caloba, L. P., Calvet, D., Calvet, S., Calvetti, M., Camacho Toro, R., Camarda, S., Camarero Munoz, D., Camarri, P., Camerlingo, M. T., Cameron, D., Camincher, C., Campanelli, M., Camplani, A., Canale, V., Canbay, A. C., Canonero, E., Cantero, J., Cao, Y., Capocasa, F., Capua, M., Carbone, A., Cardarelli, R., Cardenas, J. C. J., Carducci, G., Carli, T., Carlino, G., Carlotto, J. I., Carlson, B. T., Carlson, E. M., Carmignani, J., Carminati, L., Carnelli, A., Carnesale, M., Caron, S., Carquin, E., Carr, I. B., Carrá, S., Carratta, G., Carroll, A. M., Casado, M. P., Caspar, M., Castillo, F. L., Castillo Garcia, L., Castillo Gimenez, V., Castro, N. F., Catinaccio, A., Catmore, J. R., Cavaliere, T., Cavaliere, V., Cavalli, N., Caviedes Betancourt, L. J., Cekmecelioglu, Y. C., Celebi, E., Cella, S., Centonze, M. S., Cepaitis, V., Cerny, K., Cerqueira, A. S., Cerri, A., Cerrito, L., Cerutti, F., Cervato, B., Cervelli, A., Cesarini, G., Cetin, S. A., Chakraborty, D., Chan, J., Chan, W. Y., Chapman, J. D., Chapon, E., Chargeishvili, B., Charlton, D. G., Chatterjee, M., Chauhan, C., Che, Y., Chekanov, S., Chekulaev, S. V., Chelkov, G. A., Chen, A., Chen, B., Chen, B., Chen, H., Chen, H., Chen, J., Chen, J., Chen, M., Chen, S., Chen, S. J., Chen, X., Chen, X., Chen, Y., Cheng, C. L., Cheng, H. C., Cheong, S., Cheplakov, A., Cheremushkina, E., Cherepanova, E., Cherkaoui El Moursli, R., Cheu, E., Cheung, K., Chevalier, L., Chiarella, V., Chiarelli, G., Chiedde, N., Chiodini, G., Chisholm, A. S., Chitan, A., Chitishvili, M., Chizhov, M. V., Choi, K., Chou, Y., Chow, E. Y. S., Chu, K. L., Chu, M. C., Chu, X., Chubinidze, Z., Chudoba, J., Chwastowski, J. J., Cieri, D., Ciesla, K. M., Cindro, V., Ciocio, A., Cirotto, F., Citron, Z. H., Citterio, M., Ciubotaru, D. A., Clark, A., Clark, P. J., Clarke Hall, N., Clarry, C., Clavijo Columbie, J. M., Clawson, S. E., Clement, C., Coadou, Y., Cobal, M., Coccaro, A., Coelho Barrue, R. F., Coelho Lopes De Sa, R., Coelli, S., Colangeli, L. S., Cole, B., Collot, J., Conde Muiño, P., Connell, M. P., Connell, S. H., Conroy, E. I., Conventi, F., Cooke, H. G., Cooper-Sarkar, A. M., Corchia, F. A., Cordeiro Oudot Choi, A., Corpe, L. D., Corradi, M., Corriveau, F., Cortes-Gonzalez, A., Costa, M. J., Costanza, F., Costanzo, D., Cote, B. M., Couthures, J., Cowan, G., Cranmer, K., Cremer, L., Cremonini, D., Crépé-Renaudin, S., Crescioli, F., Cristinziani, M., Cristoforetti, M., Croft, V., Crosby, J. E., Crosetti, G., Cueto, A., Cui, H., Cui, Z., Cunningham, W. R., Curcio, F., Curran, J. R., Czodrowski, P., Da Cunha Sargedas De Sousa, M. J., Da Fonseca Pinto, J. V., Da Via, C., Dabrowski, W., Dado, T., Dahbi, S., Dai, T., Dal Santo, D., Dallapiccola, C., Dam, M., D’amen, G., D’Amico, V., Damp, J., Dandoy, J. R., Dannheim, D., Danninger, M., Dao, V., Darbo, G., Das, S. J., Dattola, F., D’Auria, S., D’Avanzo, A., David, C., Davidek, T., Dawson, I., Day-hall, H. A., De, K., De Asmundis, R., De Biase, N., De Castro, S., De Groot, N., de Jong, P., De la Torre, H., De Maria, A., De Salvo, A., De Sanctis, U., De Santis, F., De Santo, A., De Vivie De Regie, J. B., Debevc, J., Dedovich, D. V., Degens, J., Deiana, A. M., Del Corso, F., Del Peso, J., Delagrange, L., Deliot, F., Delitzsch, C. M., Della Pietra, M., Della Volpe, D., Dell’Acqua, A., Dell’Asta, L., Delmastro, M., Delsart, P. A., Demers, S., Demichev, M., Denisov, S. P., D’Eramo, L., Derendarz, D., Derue, F., Dervan, P., Desch, K., Deutsch, C., Di Bello, F. A., Di Ciaccio, A., Di Ciaccio, L., Di Domenico, A., Di Donato, C., Di Girolamo, A., Di Gregorio, G., Di Luca, A., Di Micco, B., Di Nardo, R., Di Petrillo, K. F., Diamantopoulou, M., Dias, F. A., Dias Do Vale, T., Diaz, M. A., Diaz Capriles, F. G., Didenko, A. R., Didenko, M., Diehl, E. B., Díez Cornell, S., Diez Pardos, C., Dimitriadi, C., Dimitrievska, A., Dingfelder, J., Dingley, T., Dinu, I-M., Dittmeier, S. J., Dittus, F., Divisek, M., Dixit, B., Djama, F., Djobava, T., Doglioni, C., Dohnalova, A., Dolejsi, J., Dolezal, Z., Domijan, K., Dona, K. M., Donadelli, M., Dong, B., Donini, J., D’Onofrio, A., D’Onofrio, M., Dopke, J., Doria, A., Dos Santos Fernandes, N., Dougan, P., Dova, M. T., Doyle, A. T., Draguet, M. A., Drescher, M. P., Dreyer, E., Drivas-koulouris, I., Drnevich, M., Drozdova, M., Du, D., du Pree, T. A., Dubinin, F., Dubovsky, M., Duchovni, E., Duckeck, G., Ducu, O. A., Duda, D., Dudarev, A., Duden, E. R., D’uffizi, M., Duflot, L., Dührssen, M., Duminica, I., Dumitriu, A. E., Dunford, M., Dungs, S., Dunne, K., Duperrin, A., Duran Yildiz, H., Düren, M., Durglishvili, A., Dwyer, B. L., Dyckes, G. I., Dyndal, M., Dziedzic, B. S., Earnshaw, Z. O., Eberwein, G. H., Eckerova, B., Eggebrecht, S., Egidio Purcino De Souza, E., Ehrke, L. F., Eigen, G., Einsweiler, K., Ekelof, T., Ekman, P. A., El Farkh, S., El Ghazali, Y., El Jarrari, H., El Moussaouy, A., Ellajosyula, V., Ellert, M., Ellinghaus, F., Ellis, N., Elmsheuser, J., Elsawy, M., Elsing, M., Emeliyanov, D., Enari, Y., Ene, I., Epari, S., Erland, P. A., Ernani Martins Neto, D., Errenst, M., Escalier, M., Escobar, C., Etzion, E., Evans, G., Evans, H., Evans, L. S., Ezhilov, A., Ezzarqtouni, S., Fabbri, F., Fabbri, L., Facini, G., Fadeyev, V., Fakhrutdinov, R. M., Fakoudis, D., Falciano, S., Falda Ulhoa Coelho, L. F., Fallavollita, F., Falsetti, G., Faltova, J., Fan, C., Fan, K. Y., Fan, Y., Fang, Y., Fanti, M., Faraj, M., Farazpay, Z., Farbin, A., Farilla, A., Farooque, T., Farrington, S. M., Fassi, F., Fassouliotis, D., Faucci Giannelli, M., Fawcett, W. J., Fayard, L., Federic, P., Federicova, P., Fedin, O. L., Feickert, M., Feligioni, L., Fellers, D. E., Feng, C., Feng, Z., Fenton, M. J., Ferencz, L., Ferguson, R. A. M., Fernandez Luengo, S. I., Fernandez Martinez, P., Fernoux, M. J. V., Ferrando, J., Ferrari, A., Ferrari, P., Ferrari, R., Ferrere, D., Ferretti, C., Fiacco, D., Fiedler, F., Fiedler, P., Filimonov, S., Filipčič, A., Filmer, E. K., Filthaut, F., Fiolhais, M. C. N., Fiorini, L., Fisher, W. C., Fitschen, T., Fitzhugh, P. M., Fleck, I., Fleischmann, P., Flick, T., Flores, M., Flores Castillo, L. R., Flores Sanz De Acedo, L., Follega, F. M., Fomin, N., Foo, J. H., Formica, A., Forti, A. C., Fortin, E., Fortman, A. W., Foti, M. G., Fountas, L., Fournier, D., Fox, H., Francavilla, P., Francescato, S., Franchellucci, S., Franchini, M., Franchino, S., Francis, D., Franco, L., Franco Lima, V., Franconi, L., Franklin, M., Frattari, G., Frid, Y. Y., Friend, J., Fritzsche, N., Froch, A., Froidevaux, D., Frost, J. A., Fu, Y., Fuenzalida Garrido, S., Fujimoto, M., Fung, K. Y., Furtado De Simas Filho, E., Furukawa, M., Fuster, J., Gaa, A., Gabrielli, A., Gabrielli, A., Gadow, P., Gagliardi, G., Gagnon, L. G., Gaid, S., Galantzan, S., Gallagher, J., Gallas, E. J., Gallop, B. J., Gan, K. K., Ganguly, S., Gao, Y., Garay Walls, F. M., Garcia, B., García, C., Garcia Alonso, A., Garcia Caffaro, A. G., García Navarro, J. E., Garcia-Sciveres, M., Gardner, G. L., Gardner, R. W., Garelli, N., Garg, D., Garg, R. B., Gargan, J. M., Garner, C. A., Garvey, C. M., Gassmann, V. K., Gaudio, G., Gautam, V., Gauzzi, P., Gavranovic, J., Gavrilenko, I. L., Gavrilyuk, A., Gay, C., Gaycken, G., Gazis, E. N., Geanta, A. A., Gee, C. M., Gekow, A., Gemme, C., Genest, M. H., Gentry, A. D., George, S., George, W. F., Geralis, T., Gessinger-Befurt, P., Geyik, M. E., Ghani, M., Ghorbanian, K., Ghosal, A., Ghosh, A., Ghosh, A., Giacobbe, B., Giagu, S., Giani, T., Giannini, A., Gibson, S. M., Gignac, M., Gil, D. T., Gilbert, A. K., Gilbert, B. J., Gillberg, D., Gilles, G., Ginabat, L., Gingrich, D. M., Giordani, M. P., Giraud, P. F., Giugliarelli, G., Giugni, D., Giuli, F., Gkialas, I., Gladilin, L. K., Glasman, C., Gledhill, G. R., Glemža, G., Glisic, M., Gnesi, I., Go, Y., Goblirsch-Kolb, M., Gocke, B., Godin, D., Gokturk, B., Goldfarb, S., Golling, T., Gololo, M. G. D., Golubkov, D., Gombas, J. P., Gomes, A., Gomes Da Silva, G., Gomez Delegido, A. J., Gonçalo, R., Gonella, L., Gongadze, A., Gonnella, F., Gonski, J. L., González Andana, R. Y., González de la Hoz, S., Gonzalez Lopez, R., Gonzalez Renteria, C., Gonzalez Rodrigues, M. V., Gonzalez Suarez, R., Gonzalez-Sevilla, S., Goossens, L., Gorini, B., Gorini, E., Gorišek, A., Gosart, T. C., Goshaw, A. T., Gostkin, M. I., Goswami, S., Gottardo, C. A., Gotz, S. A., Gouighri, M., Goumarre, V., Goussiou, A. G., Govender, N., Grabarczyk, R. P., Grabowska-Bold, I., Graham, K., Gramstad, E., Grancagnolo, S., Grant, C. M., Gravila, P. M., Gravili, F. G., Gray, H. M., Greco, M., Green, M. J., Grefe, C., Grefsrud, A. S., Gregor, I. M., Greif, K. T., Grenier, P., Grewe, S. G., Grillo, A. A., Grimm, K., Grinstein, S., Grivaz, J.-F., Gross, E., Grosse-Knetter, J., Guan, L., Guerrero Rojas, J. G. R., Guerrieri, G., Gugel, R., Guhit, J. A. M., Guida, A., Guilloton, E., Guindon, S., Guo, F., Guo, J., Guo, L., Guo, L., Guo, Y., Gupta, A., Gupta, R., Gurbuz, S., Gurdasani, S. S., Gustavino, G., Gutierrez, P., Gutierrez Zagazeta, L. 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G., Zhu, J., Zhu, X., Zhu, Y., Zhu, Y., Zhuang, X., Zhukov, K., Zimine, N. I., Zinsser, J., Ziolkowski, M., Živković, L., Zoccoli, A., Zoch, K., Zorbas, T. G., Zormpa, O., Zou, W., and Zwalinski, L.
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- 2024
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49. Author Correction: Analysis of magnetic field-induced breakup of ferrofluid droplets in a symmetric Y-junction microchannel
- Author
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Naseri, Parviz, Seyyedi, Seyyed Masoud, Hashemi-Tilehnoee, Mehdi, and Naeimi, Azadeh Sadat
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- 2024
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50. Bio-inspired PDMS microcantilever flow meter
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Naserifar, Naser, Naseri Karimvand, Ahmad, and Rahimi Shahmirzadi, Shayan
- Published
- 2024
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