Viscoelastic Rheological Modelling of the Lateral Dynamic Response of Ballasted Railway Tracks

This article presents a novel methodology for evaluating the response of ballasted railway tracks under train-induced loading along lateral directions. The main focus of this study is the development of a computational technique that can capture the lateral response of ballasted railway tracks, which has been ignored in past studies. The proposed approach employs a viscoelastic rheological track model in which three substructure layers are simulated using discrete masses, Hooke’s (springs) and Newtonian elements (dampers). The methodology is successfully validated against the data from experimental and analytical investigations published in the literature. Subsequently, parametric investigations are conducted to study the influence of axle load, train speed, and granular layer thickness on the track response. The results indicate that as the axle load (20 to 40 t) and train speed (70 to 200 km/h) are increased, there is a corresponding increase in track displacements (both lateral and vertical) by 100% and 26.2%, respectively. However, an increase in the granular layer thickness (0.1 to 0.75 m) reduces ballast top displacement (lateral and vertical) by 20–30%. The results demonstrate the capability of the proposed computational approach to capture the transient response of railway tracks and the influence of neighbouring layer properties on the track response. The proposed methodology can be helpful to practising railway engineers for assessing the performance of ballasted railway tracks along lateral directions

Graphene oxide nano-engineered recycled aggregate concrete for sustainable construction: A critical review

Graphene oxide (GO) has been generally accepted can improve the microstructure of concrete because it can promote cement hydration, this nano-enhancement strategy is valuable for improving the weak interfacial transition zone in recycled aggregate concrete (RAC). This paper review aims to provide a comprehensive understanding and comparison of the impact of GO on the performance and microstructure of RAC through various modification methods. Initially, the characteristics of GO and its utilization in preparing RAC are introduced. Subsequently, the effects of admixing GO on the performance of RAC and its roles within the RAC system are analyzed and discussed. Furthermore, an economic, environmental evaluation, and research limitations of GO-modified RAC are analyzed. Finally, the conclusions and potential research prospects for future investigations are outlined. The findings of this review have the potential to expand the understanding of GO-modified RAC and can serve as a source of inspiration for future research endeavors

Enhancing autonomous pavement crack detection: Optimizing YOLOv5s algorithm with advanced deep learning techniques

To enhance the safety and comfort of vehicle travel, detecting pavement cracks is a critical task in road management. This article introduces an advanced single-stage target detection method utilizing the YOLOv5s algorithm to enhance real-time performance and accuracy. Initially, Squeeze-and-Excitation Networks are integrated into the model to facilitate self-learning for improved crack characterization. Subsequently, anchors computed through the K-means clustering algorithm are closely aligned with the fracture dataset, achieving an adaptation rate of 99.9 % and enhancing the recall rate of the model. Furthermore, the inclusion of the SimSPPF module from YOLOv6 diminishes memory usage and expedites detection speed. By replacing the original nearest up-sampling method with transposed convolution, optimization of up-sampling for crack datasets is achieved. Performance assessments reveal that the refined YOLOv5s algorithm attains an F1 score of 91 %, a mean Average Precision (mAP) of 93.6 %, and a 1.54 % increase in frames per second (fps) for pavement crack detection. This enhancement in detection technology signifies a substantial advancement in the maintenance and longevity of road infrastructure

Biallelic and monoallelic variants in PLXNA1 are implicated in a novel neurodevelopmental disorder with variable cerebral and eye anomalies.

PURPOSE: To investigate the effect of PLXNA1 variants on the phenotype of patients with autosomal dominant and recessive inheritance patterns and to functionally characterize the zebrafish homologs plxna1a and plxna1b during development. METHODS: We assembled ten patients from seven families with biallelic or de novo PLXNA1 variants. We describe genotype-phenotype correlations, investigated the variants by structural modeling, and used Morpholino knockdown experiments in zebrafish to characterize the embryonic role of plxna1a and plxna1b. RESULTS: Shared phenotypic features among patients include global developmental delay (9/10), brain anomalies (6/10), and eye anomalies (7/10). Notably, seizures were predominantly reported in patients with monoallelic variants. Structural modeling of missense variants in PLXNA1 suggests distortion in the native protein. Our zebrafish studies enforce an embryonic role of plxna1a and plxna1b in the development of the central nervous system and the eye. CONCLUSION: We propose that different biallelic and monoallelic variants in PLXNA1 result in a novel neurodevelopmental syndrome mainly comprising developmental delay, brain, and eye anomalies. We hypothesize that biallelic variants in the extracellular Plexin-A1 domains lead to impaired dimerization or lack of receptor molecules, whereas monoallelic variants in the intracellular Plexin-A1 domains might impair downstream signaling through a dominant-negative effect

A Real Time Approach to Theft Prevention in the field of Transportation System

This paper report discusses a theft prevention system, which can prevent the theft and also can be track the object. This system is capable to tracking the vehicle as well as theft prevention. An R.F. module is use to exchange the information regarding vehicle and owner of the vehicle with police control room or SOS services. The vehicle can be track with the help of R.F. receiver. A DTMF based fuel lock has been attached in this system. A cell phone with SIM card has been attached with DTMF IC. The fuel flow in the vehicle can be controlled by give a call to this cell phone. This system has been controlled by a microcontroller which can make the system cost effective, low power consumption, effective and reliable

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Not AvailableComparative study was conducted to evaluate the seasonal effects of transportation of goats (Alpine × Beetle) at different flocking densities, supplemented with vitamin C in group I, vitamin C + electrolyte in group II and jaggery in group III, 3 days before transport of animal, during winter and hot humid (HH) seasons. Goats of 10–12 months old were selected from Livestock Research Centre (LRC), National Dairy Research Institute (NDRI), Karnal, India, consisted of 25 goats each, divided into high flocking density (hfd; 15) and low flocking density (lfd; 10), transported for 8 h with average speed of 25 km/h. All the animals were kept off-feed and deprived of water during the transportation period. Blood samples were taken just before transportation, immediately after transportation, 6 h, 12 h, 24 h and 2 days post-transportation from all the three groups. The blood samples were further analysed for estimation of different haematological parameters (haemoglobin (Hb), packed cell volume (PCV) and red blood cell (RBC)). Maximum values (P < 0.05) of Hb, PCV and RBC were observed just after unloading which then returned to normal values within 6–12 h post-transportation at lfd and hfd in both the seasons. Supplementation with vitamin C, vitamin C + electrolyte, and jaggery aided in reducing transportation stress but vitamin C + electrolyte combination proved more beneficial in alleviating transportation stress in the goats.Not Availabl