The Effect of Mixture Parameters on the Surface Properties of Roller Compacted Concrete (RCC) Pavements

In Turkey, the use of RCC pavements is increasing in urban and rural roads. However, a detailed study examining the effect of RCC mixture parameters on the pavement surface properties that affect road driving comfort and safety is not available in literature. In this study, in order to cover that gap in literature 12 RCC mixtures prepared with different cement dosages, aggregate gradations and water amounts were compacted by "Superpave-Gyratory-Compactor" at different levels. Later, the surface characteristics were evaluated with British pendulum and sand patch tests. It was concluded that cement dosage, water content and gradation have an effect not only in terms of strength but also in terms of pavement surface properties, and recommendations were made for RCC mixture optimization.Ülkemizde SSB kaplamaların şehir içi ve köy yollarında kullanımı gittikçe artmaktadır. Fakat yol sürüş konforunu ve güvenliğini etkileyen kaplama yüzey özelliklerine, karışım parametrelerinin etkisini inceleyen detaylı bir çalışma uluslararası literatürde mevcut değildir. Bu eksikliği gidermeye yönelik yapılan bu çalışmada, farklı çimento dozajları, agrega gradasyonları ve su oranları ile hazırlanan 12 SSB karışımı, “Superpave-Yoğurmalı-Presi” ile farklı seviyelerde sıkıştırılıp yüzey özellikleri, İngiliz pandülü ve kum yama testleriyle değerlendirilmiştir. Yapılan istatistiksel analizlerde, yoğurma sayısının etkisi görülmezken; SSB çimento dozajı, su muhtevası ve gradasyonun yalnızca mukavemet yönünden değil aynı zamanda yüzey özellikleri bakımından da etkili olduğu sonucuna varılmış ve SSB karışım optimizasyonu için öneriler getirilmiştir

Deep learning models for predicting RNA degradation via dual crowdsourcing

Medicines based on messenger RNA (mRNA) hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition (‘Stanford OpenVaccine’) on Kaggle, involving single-nucleotide resolution measurements on 6,043 diverse 102–130-nucleotide RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504–1,588 nucleotides) with improved accuracy compared with previously published models. These results indicate that such models can represent in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for dataset creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales

Deep learning models for predicting RNA degradation via dual crowdsourcing

Messenger RNA-based medicines hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition ("Stanford OpenVaccine") on Kaggle, involving single-nucleotide resolution measurements on 6043 102-130-nucleotide diverse RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1588 nucleotides) with improved accuracy compared to previously published models. Top teams integrated natural language processing architectures and data augmentation techniques with predictions from previous dynamic programming models for RNA secondary structure. These results indicate that such models are capable of representing in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for data set creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales

Identification of KIF21A mutations as a rare cause of congenital fibrosis of the extraocular muscles type 3 (CFEOM3).

PURPOSE. Three congenital fibrosis of the extraocular muscles phenotypes (CFEOM1-3) have been identified. Each represents a specific form of paralytic strabismus characterized by congenital restrictive ophthalmoplegia, often with accompanying ptosis. It has been demonstrated that CFEOM1 results from mutations in KIF21A and CFEOM2 from mutations in PHOX2A. This study was conducted to determine the incidence of KIF21A and PHOX2A mutations among individuals with the third CFEOM phenotype, CFEOM3. METHODS. All pedigrees and sporadic individuals with CFEOM3 in the authors' database were identified, whether the pedigrees were linked or consistent with linkage to the FEOM1, FEOM2, and/or FEOM3 loci was determined, and the appropriate pedigrees and the sporadic individuals were screened for mutations in KIF21A and PHOX2A. RESULTS. Twelve CFEOM3 pedigrees and 10 CFEOM3 sporadic individuals were identified in the database. The structures of eight of the pedigrees permitted the generation of meaningful linkage data. KIF21A was screened in 17 probands, and mutations were identified in two CFEOM3 pedigrees. One pedigree harbored a novel mutation (2841G-->A, M947I) and one harbored the most common and recurrent of the CFEOM1 mutations identified previously (2860C-->T, R954W). None of CFEOM3 pedigrees or sporadic individuals harbored mutations in PHOX2A. CONCLUSIONS. The results demonstrate that KIF21A mutations are a rare cause of CFEOM3 and that KIF21A mutations can be nonpenetrant. Although KIF21A is the first gene to be associated with CFEOM3, the results imply that mutations in the unidentified FEOM3 gene are the more common cause of this phenotype

Operational Measures For Energy Efficiency In Shipping

The aim of this study is to identify potential solutions to improve energy efficiency of the existing ships. To have an Ship Energy Efficiency Management Plan (SEEMP) on board has become mandatory for all ships starting from 1 January 2013. Increasing fuel prices and growing environmental concerns are driving the shipping industry to be more efficient. Therefore it is necessary to develop energy efficient operational measures