Repair of damaged aluminum truss joints of highway overhead sign structures using FRP

An innovative repair technique is introduced for aluminum truss-type highway overhead sign structures, using fibre-reinforced polymer (FRP) sheets. The welded k-joints are typically subjected to excessive fatigue-induced cracking under the effects of wind and moving traffic. The repair technique proposed in this paper utilizes longitudinal FRP layers bonded to the diagonals and wrapped around the main chord to form alternating v-patterns, followed by additional circumferential layers for anchorage. Eight tests were conducted on four full-scale specimens. Weld lines at the junction between diagonals and main chord were ground to simulate a 90% loss of joint strength. After repair, diagonals were loaded to failure in tension. The study showed that full strength of the welded joints was restored using carbon-FRP sheets. Only 70% of joint strength was restored when using glass-FRP. The strengthening technique is particularly sensitive to quality control during installation. A field application using the proposed technique was successfully completed by the New York State Department of Transportation for a cracked aluminum truss over Route 88 in NY State.     

A fuzzy approach for the earned value management

The Earned Value technique is a crucial technique in analyzing and controlling the performance of a project which allows a more accurate measurement of both the performance and the progress of a project. This paper presents a new fuzzy-based earned value model with the advantage of developing and analyzing the earned value indices, and the time and the cost estimates at completion under uncertainty. As the uncertainty is inherent in real-life activities, the developed model is very useful in evaluating the progress of a project where uncertainty arises. A small example illustrates how the new model can be implemented in reality.     

Graph Pangenomes Track Genetic Variants for Crop Improvement

Global climate change and the urgency to transform crops require an exhaustive genetic evaluation. The large polyploid genomes of food crops, such as cereals, make it difficult to identify candidate genes with confirmed hereditary. Although genome-wide association studies (GWAS) have been proficient in identifying genetic variants that are associated with complex traits, the resolution of acquired heritability faces several significant bottlenecks such as incomplete detection of structural variants (SV), genetic heterogeneity, and/or locus heterogeneity. Consequently, a biased estimate is generated with respect to agronomically complex traits. The graph pangenomes have resolved this missing heritability and provide significant details in terms of specific loci segregating among individuals and evolving to variations. The graph pangenome approach facilitates crop improvements through genome-linked fast breeding.     

The Effect of Intensity-Modulated Radiotherapy to the Head and Neck Region on the Oral Innate Immune Response and Oral Microbiome: A Prospective Cohort Study of Head and Neck Tumour Patients

Neutrophils, also known as polymorphonuclear leukocytes (PMNs), form a significant component of the innate host response, and the consequence of the interaction between the oral microbiota and PMNs is a crucial determinant of oral health status. The impact of radiation therapy (RT) for head and neck tumour (HNT) treatment on the oral innate immune system, neutrophils in particular, and the oral microbiome has not been thoroughly investigated. Therefore, the objective of this study was to characterize RT-mediated changes in oral neutrophils (oPMNs) and the oral microbiome in patients undergoing RT to treat HNTs. Oral rinse samples were collected prior to, during and post-RT from HNT patients receiving RT at Dental Oncology at Princess Margaret Cancer Centre. The oPMNs counts and activation states were analysed using flow cytometry, and the oral microbiome was analysed using 16S rRNA gene sequencing. Statistically significant (p < 0.05) drops in oPMN counts and the activation states of the CD11b, CD16, CD18, CD64 and H3Cit markers from pre-RT to post-RT were observed. Moreover, exposure to RT caused a significant reduction in the relative abundance of commensal Gram-negative bacteria and increased the commensal Gram-positive microbes. Ionizing radiation for the treatment of HNTs simultaneously decreased the recruitment of oPMNs into the oral cavity and suppressed their activation state. The oral microbiome composition post-RT was altered significantly due to RT which may favour the colonization of specific microbial communities unfavourable for the long-term development of a balanced oral microbiome.     

Protein–Protein Interaction Prediction for Targeted Protein Degradation

Protein–protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, machine learning-based methods have been developed as screening tools. While they are computationally more efficient than traditional docking methods and thus allow rapid execution, these tools have so far primarily been based on sequence information, and they are therefore limited in their ability to address spatial requirements. In addition, they have to date not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation.     

Elaboration of Al2O3/PTFE icephobic coatings for protecting aluminum surfaces

In order to protect aluminum ground wires and phase conductors of overhead power lines against ice adhesion and excessive accretion, for ensuring safe and reliable power transmission during winter periods, a new coating with icephobic characteristics and satisfactory mechanical properties was developed. The method consisted in depositing an extremely adherent poly(tetrafluoroethylene) or PTFE coating on an Al₂O₃ underlayer produced by anodisation in either a phosphoric or an oxalic acid electrolyte. PTFE impregnation was carried out at low temperature (320 °C) and coating adhesion was assessed using tape and bend tests. These treatments resulted in highly hydrophobic surfaces with water contact angles lying between 130° and 140°. Ice shear stress was reduced by almost 2.5 times, and the PTFE coatings remained active after several ice shedding events. Morphologies and chemical compositions were studied using scanning electron microscopy, energy dispersive X-Ray analysis, as well as Fourier Transform Infra Red and X-Ray photoelectron spectroscopy.     

Application of ion-engineered Persian Gulf seawater in EOR:effects of different ions on interfacial tension,contact angle,zeta potential,and oil recovery

In this study,we initially performed interfacial tension(IFT)tests to investigate the potential of using the Persian Gulf sea-water(PGSW)as smart water with dif...     

Wettability alteration and retention of mixed polymer-grafted silica nanoparticles onto oil-wet porous medium

Enhanced oil recovery(EOR)processes are applied to recover trapped or residual oil in the reservoir rocks after primary and secondary recovery methods.Changing ...     

FPGA based fast and high-throughput 2-slow retiming 128-bit AES encryption algorithm

This paper presents a high throughput digital design of the 128-bit Advanced Encryption Standard (AES) algorithm based on the 2-slow retiming technique on FPGA. The C-slow retiming is a well-known optimization and high performance technique. It can enhance designs with feedback loops and automatically rebalances the registers in the design. The C-slow retiming can break the critical path of the design into finer pieces to improve the throughput of the design. The complexity of the C-slow retiming on FPGA is to find the best register allocation in the data path of the design so that by increasing the number of registers, relocation of the registers to balance the AES architecture be in the best mode, and the critical path be optimally pipelined and improved. In this paper, architecture of the AES algorithm is implemented in the gate level by high-speed and breakable structures that are desirable for the 2-slow retiming. The Mix-columns transformation is implemented based on multiplication by constants 2 and 3 modules with combinational logic circuits. This work has been successfully verified and synthesized using Xilinx ISE 11 byVirtex-5, XC5VLX85 FPGA. The proposed implementation achieves a high throughput of 86 Gb/s and high maximum operation frequency of 671.524 MHz whereas the highest throughput and the highest operation frequency reported in the literature are 73.737 Gb/s and 576.07 MHz, respectively.