Polyfluorinated crosslinker-based solid polymer electrolytes for long-cycling 4.5 V lithium metal batteries by Tang, Lingfei, Chen, Bowen, Zhang, Zhonghan, Ma, Changqi, Chen, Junchao, Huang, Yage, Zhang, Fengrui, Dong, Qingyu, Xue, Guoyong, Chen, Daiqian, Hu, Chenji, Li, Shuzhou, Liu, Zheng, Shen, Yanbin, Chen, Qi, Chen, Liwei

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Error measurements for a quantum annealer using the one-dimensional Ising model with twisted boundaries by Chancellor, Nicholas, Crowley, Philip J. D., Durić, Tanja, Vinci, Walter, Amin, Mohammad H., Green, Andrew G., Warburton, Paul A., Aeppli, Gabriel

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Haemodynamic changes in visceral hybrid repairs of type III and type V thoracoabdominal aortic aneurysms by Ong, Chi Wei, Wee, Ian J. Y., Cui, Fangsen, Xu, Xiao Yun, Richards, Arthur Mark, Leo, Hwa Liang, Choong, Andrew M. T. L.

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Additive manufacturing of alloys with programmable microstructure and properties by Gao, Shubo, Li, Zhi, Van Petegem, Steven, Ge, Junyu, Goel, Sneha, Vas, Joseph Vimal, Luzin, Vladimir, Hu, Zhiheng, Seet, Hang Li, Sanchez, Dario Ferreira, Van Swygenhoven, Helena, Gao, Huajian, Seita, Matteo

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Free-electron crystals for enhanced X-ray radiation by Wong, Wesley Lee Wei, Shi, Xihang, Karnieli, Aviv, Lim, Jeremy, Kumar, Suraj, Carbajo, Sergio, Kaminer, Ido, Wong, Liang Jie

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Dynamic chloride ion adsorption on single iridium atom boosts seawater oxidation catalysis by Duan, Xinxuan, Sha, Qihao, Li, Pengsong, Li, Tianshui, Yang, Guotao, Liu, Wei, Yu, Ende, Zhou, Daojin, Fang, Jinjie, Chen, Wenxing, Chen, Yizhen, Zheng, Lirong, Liao, Jiangwen, Wang, Zeyu, Li, Yaping, Yang, Hongbin, Zhang, Guoxin, Zhuang, Zhongbin, Hung, Sung-Fu, Jing, Changfei, Luo, Jun, Bai, Lu, Dong, Juncai, Xiao, Hai, Liu, Wen, Kuang, Yun, Liu, Bin, Sun, Xiaoming

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A dynamic knowledge graph approach to distributed self-driving laboratories by Bai, Jiaru, Mosbach, Sebastian, Taylor, Connor J., Karan, Dogancan, Lee, Kok Foong, Rihm, Simon D., Akroyd, Jethro, Lapkin, Alexei A., Kraft, Markus

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Biaxial strain induced OH engineer for accelerating alkaline hydrogen evolution by Zhang, Tao, Ye, Qitong, Han, Zengyu, Liu, Qingyi, Liu, Yipu, Wu, Dongshuang, Fan, Hong Jin

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A comparative analysis of ENCODE and Cistrome in the context of TF binding signal by Perna, Stefano, Pinoli, Pietro, Ceri, Stefano, Wong, Limsoon

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Harnessing full-text publications for deep insights into C. elegans and Drosophila biomaps by Arulprakasam, Karthick Raja, Toh, Janelle Wing Shan, Foo, Herman, Kumar, Mani R., Kutevska, An-Nikol, Davey, Emilia Emmanuelle, Mutwil, Marek, Thibault, Guillaume

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The stalks of the motor A1A0 ATP synthase come into the focus : structural insight into subunits E, F and H by Gayen, Shovanlal

“…The stalk parts have an important role for the regulation of this class of enzymes and are separated into a central part (C:D:F) and peripheral parts (Ex:H2:aN). …”
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Layer-by-layer assembled micro and nano carriers for drug delivery applications by Mutukumaraswamy Shaillender

“…Chapter 3 explains the general methods for fabrication of protein loaded LbL coated CaCO3 particles and their application to treat breast tumors. Up regulation of cysteine proteases in tumor cells constitute a major role in extra cellular matrix (ECM) degradation and metastatic invasion. …”
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Photo-driven smart polymers and their molecular self-assembly by Hu, Jinhua

“…The different photo-isomerization behaviours of the azobenzene derivatives were identified to have contributed to regulation of the pulsation behaviour. The extraordinarily large change in vesicle size was originated from an isomerization-driven molecular assembly upon UV-visible irradiation cycles, and was associated with substantial transmembrane solvent transport. …”
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Neuronal control of cardiac excitability in pro-hypertensive states by Larsen, H

“…The work in this thesis characterises the Ca²⁺ dysfunction and regulation at the membrane level. Further, it employs physiologically coupled sympathetic neurons and ventricular myocytes to determine the cellular driver of cardiac dysautonomia in the pro-hypertensive state.…”

Spatial and subcellular gene expression patterns in myogenic differentiation and muscle pathology by Chwalenia, KM

“…</p> <p>Within the muscle tissue myomiRs are known to regulate the process of myogenesis which supports skeletal muscle turnover in DMD. …”

Quantifying the energy consumption of the water use cycle by Cardenes Trujillo, I

“…Thirdly, it evaluates trade-offs between energy costs and changing effluent quality regulations in wastewater resources. The Thames River basin, in the south-east of England, is used as a case study to illustrate the approach.…”

The development and validation of digital medical education environment (digimee) instrument in measuring online learning environment for undergraduate medical students by Haris, Noor-I-Kiran

“…Recommendations included improving digital skills training, personalized feedback, and platform communication, alongside better self-regulation support and more effective applications.These insights led to a list of recommendations for the university. …”
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Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage by Vignolle, Gabriel A., Bauerstätter, Priska, Schönthaler, Silvia, Nöhammer, Christa, Olischar, Monika, Berger, Angelika, Kasprian, Gregor, Langs, Georg, Vierlinger, Klemens, Goeral, Katharina

“…Various explainable ML techniques&mdash;including statistical, regularization, deep learning, decision trees, and Bayesian methods&mdash;were employed to predict PHVD development and survival and to discover disease-specific protein biomarkers. …”
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从频率效应看上古汉语*-s后缀 = A study of *-s suffix in archaic Chinese based on frequency effect by 黄颖瑜 Ng, Yin Yee

“…Among these morphological processes, there are still rooms for discussion regarding the regularity of si sheng bie yi(四声别义). Many scholars agree that *-s suffix is the source for si sheng bie yi(四声别义). …”
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Online learning with kernels by Fan, Haijin

“…On the other hand, to update the kernel weight, the traditional linear or nonlinear online learning algorithms are extended to RKHS to develop types of kernel online learning algorithms, such as the kernel recursive least square algorithms and the kernel (regularized) least mean square algorithms which incorporate suitable sparsification methods to select their kernel centers. …”
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