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100001 - 100010 von 105007

	100001	Prediction of Potential Urban Hot Spots in Urban Environments Using Convolutional Neural Networks and Remote Sensing Techniques Enthalten in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. X-4-2024, 2024: 195-202. 8 S.
	100002	Prediction of practical performance in preclinical laboratory courses - the return of wire bending for admission of dental students in Hamburg Enthalten in GMS Zeitschrift für medizinische Ausbildung Bd. 31, 15.05.2014, Nr. 2
	100003	Prediction of pre-eclampsia: a protocol for systematic reviews of test accuracy Enthalten in BMC pregnancy and childbirth Bd. 6, 19.10.2006, Nr. 1, date:12.2006: 1-8
	100004	Prediction of reliability on thermoelectric module through accelerated life test and Physics-of-failure Enthalten in Electronic materials letters Bd. 7, 18.10.2011, Nr. 3, date:9.2011: 271-275
	100005	Prediction of remdesivir resistance in COVID-19 illness: Need for development of clinical laboratory test Enthalten in Indian journal of clinical biochemistry Bd. 36, 10.6.2021, Nr. 4, date:10.2021: 498-499
	100006	Prediction of Rupture by Complete Blood Count in Tubal Ectopic Pregnancies Treated with a Single-Dose Methotrexate Protocol Enthalten in Revista brasileira de ginecologia e obstetrícia Bd. 45, 2023, Nr. 09: e503-e510
	100007	Prediction of shear wave velocity with standard penetration test and Menard pressuremeter test: The Eskişehir (Turkey) case Enthalten in Environmental earth sciences Bd. 84, 29.7.2025, Nr. 16, date:8.2025: 1-19
	100008	Prediction of the dose range for adverse neurological effects of amiodarone in patients from an in vitro toxicity test by in vitro–in vivo extrapolation Enthalten in Archives of toxicology Bd. 95, 19.2.2021, Nr. 4, date:4.2021: 1433-1442
	100009	Prediction of the long-term flexural behavior of glued laminated bamboo using accelerated creep test Enthalten in Wood science and technology Bd. 57, 25.8.2023, Nr. 5, date:9.2023: 1139-1155
	100010	Prediction of the R 3 Test-Based Reactivity of Supplementary Cementitious Materials: A Machine Learning Approach Utilizing Physical and Chemical Properties Enthalten in International journal of concrete structures and materials Bd. 18, 1.11.2024, Nr. 1, date:12.2024: 1-22

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