Determination of the surface sterilization protocol and optimization of ficus carica callus induction: Unveiling the potential of auxins and low kinetin concentration on callogenes... by Sin, Phin Yin, Tan, Suat Hian, Lim, Hooi Thong, Putri Shareen, Rosman, Karim, Md. Rezaul

“…In conclusion, a lower cytokinin concentration proved optimal for maximum callus induction when combined with auxin, providing valuable insights for improving F. carica micropropagation protocols.…”
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Improved methodology for evaluating adversarial robustness in deep neural networks by Lee, Kyungmi,(Computer scientist)Massachusetts Institute of Technology.

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NMR detection and conformational dependence of two, three, and four-bond isotope shifts due to deuteration of backbone amides by Alexandrescu, Andrei T., Dregni, Aurelio J., Teschke, Carolyn M.

“…Abstract NMR isotope shifts occur due to small differences in nuclear shielding when nearby atoms are different isotopes. For molecules dissolved in 1:1 H2O:D2O, the resulting mixture of N-H and N-D isotopes leads to a small splitting of resonances from adjacent nuclei. …”
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Development and Application of a Data-Driven Signal Detection Method for Surveillance of Adverse Event Variability Across Manufacturing Lots of Biologics by Wilde, Joshua T., Springs, Stacy, Wolfrum, Jacqueline M., Levi, Retsef

“…Results Extensive method validation on simulated data indicated that HMMScan is able to correctly detect the presence or absence of variable manufacturing and supply chain conditions for contiguous sequences of 100 lots or more when changes in these conditions have a meaningful impact on AE rates. …”
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Integrating quantitative and qualitative methodologies to build a national R&D plan using data envelopment analysis based on R&D stakeholders’ perspectives by Park, Wan, Kim, Sang-Gook

“…This study proposes a new approach to compensate for the difference between theoretical virtual prices and actual prices in data envelopment analysis. In particular, when comparing the only quantitative results on investment priorities with analysis results by additionally reflecting the opinions of experts in each sector, we found that the Korean government’s investment priorities in the smart farm field are considerably distorted. …”
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Rheo-PIV of yield-stress fluids in a 3D-printed fractal vane-in-cup geometry by Medina-Bañuelos, Esteban F., Marín-Santibáñez, Benjamín M., Chaparian, Emad, Owens, Crystal E., McKinley, Gareth H., Pérez-González, José

“…We describe the velocity distributions in all cases and show that the fVIC produces an almost axisymmetric flow field and rotation rate-independent “effective radius” when used with both the Newtonian fluid and the microgel. …”
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Master curves for FENE-P fluids in steady shear flow by Yamani, Sami, McKinley, Gareth H.

“…We suggest that reporting the magnitude of Wi/L when performing stability analyses or simulating shear-dominated flows with the FENE-P model will help clarify finite extensibility effects.…”
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Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data by Xu, Xuhai, Yao, Bingsheng, Dong, Yuanzhe, Gabriel, Saadia, Yu, Hong, Hendler, James, Ghassemi, Marzyeh, Dey, Anind K., Wang, Dakuo

“…However, there is still a significant gap in research when it comes to understanding and enhancing the capabilities of LLMs in the field of mental health. …”
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A large-area lithium metal–carbon nanotube film for precise contact prelithiation in lithium-ion batteries by Wang, Chao, Yang, Fangzhou, Wan, Wang, Wang, Shihe, Zhang, Yongyi, Huang, Yunhui, Li, Ju

“…To achieve prelithiation of the graphite anode when paired with commercial LFP electrodes of ∼3.3 mA h cm−2, our Li-CNT film significantly enhances the initial Coulombic efficiency of the LFP||Gr full cell from 89% to 100%, fully compensating for the initial loss of active lithium ions caused by solid electrolyte interface formation. …”
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A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics by Barthel Sorensen, B., Charalampopoulos, A., Zhang, S., Harrop, B. E., Leung, L. R., Sapsis, T. P.

“…For the former, our method enables the quantification of events that have return period two orders longer than the training data. For the latter, when trained on 8 years of ERA5 data, our approach is able to correct the coarse E3SM output to closely reflect the 36‐year ERA5 statistics for all prognostic variables and significantly reduce their spatial biases.…”
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Direct Comparison of Numerical Simulations and Experiments of $$\hbox {CO}_2$$ Injection and Migration in Geologic Media: Value of Local Data and Forecasting Capability by Saló-Salgado, Lluís, Haugen, Malin, Eikehaug, Kristoffer, Fernø, Martin, Nordbotten, Jan M., Juanes, Ruben

“…We evaluate (1) the value of prior knowledge of the system, expressed in terms of ex situ measurements of the tank sands’ multiphase flow properties (local data), with respect to simulation accuracy; and (2) the forecasting capability of history-matched numerical models, when applied to different settings. We match three versions of a numerical simulation model—each with access to an increasing level of local data—to a CO2 injection experiment in Tank 1 (89.7×47×1.05 cm). …”
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Inclusive and differential cross section measurements of t t ¯ b b ¯ production in the lepton+jets channel at √𝑠 = 13 TeV by Hayrapetyan, A., Tumasyan, A., Adam, W., Andrejkovic, J. W., Bergauer, T., Chatterjee, S., Damanakis, K., Dragicevic, M., Escalante Del Valle, A., Hussain, P. S., Jeitler, M., Krammer, N., Liko, D., Mikulec, I., Schieck, J., Schöfbeck, R., Schwarz, D.

“…The only exception is when using a particular choice of dynamic renormalization scale, μ R = 1 2 ∏ i = t , t ¯ , b , b ¯ m T , i 1 / 4 $$ {\mu}_{\textrm{R}}=\frac{1}{2}{\prod}_{i=\textrm{t},\overline{\textrm{t}},\textrm{b},\overline{\textrm{b}}}{m}_{\textrm{T},i}^{1/4} $$ , where m T , i 2 = m i 2 + p T , i 2 $$ {m}_{\textrm{T},i}^2={m}_i^2+{p}_{\textrm{T},i}^2 $$ are the transverse masses of top and bottom quarks. …”
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Oral Carbon Monoxide Enhances Autophagy Modulation in Prostate, Pancreatic, and Lung Cancers by Bi, Jianling, Witt, Emily, McGovern, Megan K., Cafi, Arielle B., Rosenstock, Lauren L., Pearson, Anna B., Brown, Timothy J., Karasic, Thomas B., Absler, Lucas C., Machkanti, Srija, Boyce, Hannah, Gallo, David, Becker, Sarah L., Ishida, Keiko, Jenkins, Joshua, Hayward, Alison, Scheiflinger, Alexandra, Bodeker, Kellie L., Kumar, Ritesh, Shaw, Scott K., Jabbour, Salma K., Lira, Vitor A., Henry, Michael D., Tift, Michael S., Otterbein, Leo E., Traverso, Giovanni, Byrne, James D.

“…A novel, gas‐entrapping material containing CO in a pre‐clinical model is applied and demonstrated that CO can dramatically increase the cytotoxicity of autophagy inhibitors and significantly inhibit the growth of tumors when used in combination. These data support the notion that safe, therapeutic levels of CO can markedly enhance the efficacy of autophagy inhibitors, opening a promising new frontier in the quest to improve cancer therapies.…”
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Simulations of radiatively cooled magnetic reconnection driven by pulsed power by Datta, Rishabh, Crilly, Aidan, Chittenden, Jeremy P., Chowdhry, Simran, Chandler, Katherine, Chaturvedi, Nikita, Myers, Clayton E., Fox, William R., Hansen, Stephanie B., Jennings, Chris A., Ji, Hantao, Kuranz, Carolyn C., Lebedev, Sergey V., Uzdensky, Dmitri A., Hare, Jack D.

“…The reconnection layer undergoes radiative collapse when the radiative losses exceed the rates of ohmic and compressional heating (cooling rate/hydrodynamic transit rate = 𝜏−1cool/𝜏−1𝐻≈100 ); this generates a cold strongly compressed current sheet, leading to an accelerated reconnection rate, consistent with theoretical predictions. …”
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Live Verification in an Interactive Proof Assistant by Gruetter, Samuel, Fukala, Viktor, Chlipala, Adam

“…We prototyped our technique in the interactive proof assistant Coq, so our framework creates machine-checked proofs that the developed functions satisfy their specifications when executed according to the formal semantics of the source language. …”
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Predicted Losses of Sulfur and Selenium in European Soils Using Machine Learning: A 2 Call for Prudent Model Interrogation and Selection by Jones, Gerrad D., Insinga, Logan, Droz, Boris, Feinberg, Aryeh, Stenke, Andrea, Smith, Jo, Smith, Pete, Winkel, Lenny H. E.

“…Other models exhibited overfitting even when differences in training and testing performance were low or non-existent. …”
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Ex Post Path Choice Estimation for Urban Rail Systems Using Smart Card Data: An Aggregated Time-Space Hypernetwork Approach by Mo, Baichuan, Ma, Zhenliang, Koutsopoulos, Haris N, Zhao, Jinhua

“…We prove that the solution to the original problem is the same as the decomposed problem (i.e., the fixed point) when passenger path choices follow the predefined behavior model. …”
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Near-zero environmental impact aircraft by Prashanth, Prakash, Elmourad, Jad, Grobler, Carla, Isaacs, Stewart, Zahid, Syed Shayan, Abel, James, Falter, Christoph, Fritz, Thibaud, Allroggen, Florian, Sabnis, Jayant S., Eastham, Sebastian D., Speth, Raymond L., Barrett, Steven R. H.

“…Aviation's impact on surface air quality, which is comparable to aviation's climate impact when monetized, further heightens this challenge. …”
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A neural clock underlying the temporal dynamics of an auditory memory by Bahle, Andrew H.

“…In this thesis I study the neural control of vocal imitation in the songbird Taengiopia guttata, focusing on the first stage of imitation when animals must form a temporally structured sensory memory, or template, of the action to be imitated. …”
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Orbital stability in a classical pilot-wave system by Liu, Nicholas Z.

“…We find that orbital quantisation is most pronounced when the waves generated by each bounce decay slowly. …”
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