Conventional scan body vs. scan bodies with auxiliary geometric devices: an in vitro study for edentulous full-arch implant impressions by Francesco Gianfreda, Carlo Raffone, Mirko Martelli, Alberto Pitino, Vito Carlo Alberto Caponio, Patrizio Bollero

“…The AGD was used to reduce positional errors during the scan process. Data were processed using Exocad and GOM Inspect Professional software, aligning scan body library files with mesh data using a best-fit algorithm. …”
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Comparative Analysis of Novel View Synthesis and Photogrammetry for 3D Forest Stand Reconstruction and Extraction of Individual Tree Parameters by Guoji Tian, Chongcheng Chen, Hongyu Huang

“…However, it can lead to reconstruction errors in the ground area when the input views are limited. …”
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Rare homozygous CNPY3 variant causing autosomal recessive developmental and epileptic encephalopathy 60: A case report by Nohela Rehman, Zohra Hasan Ali, Mahrukh Nasir, Salman Kirmani

“…This highlights the need for further research to understand CNPY3-associated phenotypes and its role in neuronal activity. …”
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Assessing the spatial-temporal performance of machine learning in predicting grapevine water status from Landsat 8 imagery via block-out and date-out cross-validation by Eve Laroche-Pinel, Vincenzo Cianciola, Khushwinder Singh, Gaetano A. Vivaldi, Luca Brillante

“…The results of the study demonstrate that machine learning is accurate in predicting vine water status spatially within the training measurement dates with low errors (NRMSEΨstem = 2.7 %, NRMSEgs = 16.2 %, NRMSEAN = 11.2 %) and a high degree of accuracy (R2 greater than 0.8 in the prediction of all three measurements) as assessed by block-out cross-validation. …”
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EXPERIENCE SANITARY-EPIDEMIOLOGICAL EXAMINATION PROJECT OF PLACING A SOURCE OF IONIZING RADIATION (GENERATING) IN HEALTH CARE INSTITUTIONS by I. A. Rakitin, A. L. Zel’din, V. B. Karpov

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Basic ENT training for enhanced healthcare worker knowledge, attitudes and practices in a resource-limited setting: a pre-post and post-post cross-sectional study by Chester Kalinda, Colleen Aldous, Charles Michelo, Lufunda Lukama, Sinoya Mbewe

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Silage Maize Identification Using a Temporal Difference-Based Model with Sentinel-2 Data: Insights from a Harvest-Based and Temporally Transferable Approach by Zhenyu Lin, Ran Huang, Sihan Tan, Lingbo Yang, Jingfeng Huang, Lijun Su, Zhichao Hu

“…In response to the limited research on silage maize classification in China and the lack of data support for refined agricultural and livestock management, this study proposes a Temporal Difference-based Silage Maize Identification Model (TempDiff-SMID) using the Google Earth Engine (GEE) platform. …”
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Comparison of Self-Test Refraction, Autorefraction, and Subjective Refraction by Kós AFDSN, Moscovici BK, Vilar C, Chaves MAPD, Da Silva MGB, Neuwald MA, De Medeiros AL, Lake JC, Motta AFP, Tzelikis PFDM, Carricondo PC, Gouvea L, Nosé W, Hida WT

“…Abraão Ferreira de Sousa Neto Kós,1 Bernardo Kaplan Moscovici,2,3 Cesar Vilar,1 Mario Augusto Pereira Dias Chaves,4 Maíra Gomes Barbosa Da Silva,1 Maurício Agne Neuwald,1 André Lins De Medeiros,1 Jonathan Clive Lake,1 Antonio Francisco Pimenta Motta,5 Patrick Frensel de Moraes Tzelikis,1 Pedro Carlos Carricondo,6 Larissa Gouvea,7 Walton Nosé,2 Wilson Takashi Hida1,4 1Hospital Oftalmologico de Brasília - HOB, Brasilia, Brasilia, Brazil; 2Department of Ophthalmology and Visual Sciences, Federal University of Sao Paulo UNIFESP, São Paulo, Brazil; 3Department of Ophthalmology, Hospital Visão Laser, Santos, Brazil; 4Department of Ophthalmology, Hospital Oftalmologico da Paraiba, Paraiba, Brazil; 5Department of Ophthalmology, Renato Ambrosio Research Center CEORA, Rio de Janeiro, Brazil; 6Department of Ophthalmology, University of Sao Paulo HC-FM USP, São Paulo, Brazil; 7Storm Eye Institute, Medical University of South Carolina, Charleston, SC, USACorrespondence: Bernardo Kaplan Moscovici, Email bernardokaplan@yahoo.com.brPurpose: To compare the performance of a self-refraction device (EyeQue Vision Check 2, EyeQue, USA) with autorefraction (OPD-Scan III, NIDEK) and subjective cycloplegic refraction in measuring refractive errors.Methods: This descriptive study included 80 eyes from 40 patients. …”
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Large-Eddy Simulation of Dry and Moist Atmospheric Boundary Layers and Analysis of the Model Convergence by Yixin ZHANG, Xindong PENG

“…By using the large eddy simulation （LES） version of the Weather Research and Forecasting （WRF） model， vertical structure and the feature of turbulent transportation of shallow-convective-cloud-topped atmospheric convective boundary layer was simulated in addition to the idealized dry convective boundary layer on oceanic surface.Numerical convergence of LES model was analyzed with the model results in different resolutions.The results showed that the dry convective boundary layer was vigorous.Approximately 1.15 km of the planetary boundary layer height was simulated with the LES in different resolutions.Vertical uniform structure of the averaged potential temperature （θ）， mixing ratio of water vapor （qv） and horizontal wind （u and v） were shown in the idealized dry convective boundary layer but with large vertical gradients in the near surface layer and the top of the boundary layer.Higher resolution model resolved more detailed structure of convective bubbles within the dry convective boundary layer， larger variance of potential temperature in the entrainment layer， vertical uniform distribution of the averaged quantities within the simulated mixed layer extending more closing to the surface， and the errors in LES model under coarse resolution were mainly concentrated in the lower boundary layer and near the inversion layer.The higher resolution LES model showed larger resolved sensible heat flux while the total flux remained.In the case of shallow-convective-cloud-topped boundary layer， obvious different boundary layer structure was displayed in comparison with that of the dry convective boundary layer.Conditionally unstable layer existed over the mixing layer， and mixing layer height dropped.Vertical profiles of averaged θ， qv， u and v showed similar structure in the mixing layer as that in the convective boundary layer.The averaged meteorological quantities in the lower mixing layer were uniformly distributed in vertical direction.In the cloudy layer， however， positive heat flux and vertical turbulent kinetic energy appeared.Negative heat flux was observed from the top of mixing layer to the lower cloudy layer， which reflected the weak inverse temperature and entrainment at top of mixing layer.Lower-resolution model simulated more deviations of the top of temperature inversion， and mean wind velocity and fluxes near surface.The large eddy simulation model tended to converge at 40 m resolution in the vigorously developing dry convective boundary layer case while it converged at 30 m resolution in the shallow-convective-cloud-topped boundary layer.The atmospheric boundary layer with lower mixing layer height needs to be simulated using a higher resolution LES model.…”
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Elemental composition of placental cotyledons in cows with retained placenta by Safonov V.A., Yakimenko V.N., Chernitskiy A.E.

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Evaluation and improvement of nursing undergraduates’ informatics competencies using a hybrid multi-criteria decision-making model by Chao Liu, YouYou Hong, Wei-Ling Hu, Lili Feng, Yen-Ching Chuang, Bing-Long Wang

“…(C 21),” and “Paying attention to the importance of information technology in clinical decision-making and preventing errors or facilitating patient care coordination (C 32).” …”
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Modeling Triatomic Biosignatures: Ozone and Isotopomers by Thomas M. Cross, David M. Benoit, Marco Pignatari

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Migraine and stroke: correlation, coexistence, dependence - a modern perspective by Michał Borończyk, Anna Zduńska, Julia Węgrzynek-Gallina, Olga Grodzka, Anetta Lasek-Bal, Izabela Domitrz

“…Furthermore, migraine may serve as a stroke mimic (condition presenting with symptoms similar to ischemic stroke) or a stroke chameleon (unrecognized stroke misdiagnosed as migraine), leading to significant diagnostic and treatment errors. Conclusions The interplay between migraine and stroke is complex, involving shared pathophysiology and overlapping risk factors. …”
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Evaluating the role of AI-simulated patients compared with peer-to-peer learning models in the enhancement of medical education: is it beyond theoretical functionality? by Pegin Poulose

“…Introduction: This research study evaluated the effectiveness of utilising patients simulated through artificial intelligence (AI) for medical education, and the role of AI as a learning tool compared with traditional peer-to-peer formats. …”
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Bioinformatics-based identification of CTSS, DOK2, and ENTPD1 as potential blood biomarkers of schizophrenia by Lei Zhang, Jiale Zhang, Na Wang, Chenwei Liu, Shuting Wang, Xiaotao Dong, Lu Yang, Xiaohong Bao, Xiaobo Nie, Jicheng Li

“…The diagnosis primarily relies on clinical assessment and patient history, a method that is inherently subjective and prone to errors, potentially leading to diagnostic delays. …”
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Evaluation of the viability and functionality of human peripheral blood mononuclear cells cryopreserved up to 2 years in animal-protein-free freezing media compared to the FBS-supp... by Frédérique Jantet-Blaudez, Joseline Ruiz, Sylviane Gautheron, Anke Pagnon

“…CryoStor CS7.5, while showing promising results, was eliminated due to being a mixture of CS10 and CS5, which could potentially introduce errors in preparation. Media with < 7.5% DMSO showed significant viability loss and were eliminated after the initial assessments. …”
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Comparison of Brightness Temperature Simulations Based on Different Land Surface Models with FY-4A/AGRI Measurements by Liu Bingtong, Li Xin, Li Yubin, Qin Zhengkun, Liu Weiguang

“…Lastly, acknowledging the superior accuracy of experiment using Noah-MP model, the study statistically analyzes the brightness temperature biases and standard deviations of AGRI surface-sensitive channels across 5 typical surface types in eastern China as a reference for quantifying errors and correcting biases in the assimilation of satellite data.…”
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Professor Erik Romanovich Hesse (1883–1938) in the history of Russian surgery and transfusiology by Al. A. Kurygin, V. S. Dovganyuk, V. V. Semenov

“…He was the author and editor of the first two-volume manual in the country «General Surgery» (1928), the first Soviet three-volume manual «Special Surgery» (1937), the editor of the four-volume manual «Errors, dangers and unforeseen complications in the treatment of surgical diseases» (1936–1937). …”
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Advanced multi-label brain hemorrhage segmentation using an attention-based residual U-Net model by Xinxin Lin, Enmiao Zou, Wenci Chen, Xinxin Chen, Le Lin

“…IoU values were also high, indicating precise segmentation and minimal boundary errors. Conclusions The ResUNet model outperformed standard U-Net variants, achieving higher multi-label segmentation accuracy. …”
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Choosing appropriate tools and referral criteria for vision screening of children aged 4–5 years in Canada: a quantitative analysis by Kevin Thorpe, Mayu Nishimura, Agnes Wong, Ashley Cohen, Daphne Maurer

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