Flow field analysis of a pentagonal-shaped bridge deck by unsteady RANS

Long-span cable-stayed bridges are susceptible to dynamic wind effects due to their inherent flexibility. The fluid flow around the bridge deck should be well understood for the efficient design of an aerodynamically stable long-span bridge system. In this work, the aerodynamic features of a pentagonal-shaped bridge deck are explored numerically. The analytical results are compared with past experimental work to assess the capability of two-dimensional unsteady RANS simulation for predicting the aerodynamic features of this type of deck. The influence of the bottom plate slope on aerodynamic response and flow features was investigated. By varying the Reynolds number (2 × 104 to 20 × 104) the aerodynamic behavior at high wind speeds is clarified

Theoretical Investigation on the Impact of Two HDR Dampers on First Modal Damping Ratio of Stay Cable

Stay cables are one of the vital components of a cable-stayed bridge. Due to their flexible nature, stay cables are vulnerable to external excitation and often vibrate with large amplitude under wind action which leads to the fatigue failure of the cables. To suppress such kind of large amplitude vibration by improving the damping ratio of the cable various dampers such as magnetorheological damper, friction damper; oil damper; or high damping rubber (HDR) damper are utilized and gained popularity over time. This paper focuses on improving the damping ratio of stay cables using a combination of two HDR dampers. First, the theoretical model is formulated considering cable bending stiffness to evaluate the damping effect of cable-HDR dampers system. Then, the impact of various design parameters of HDR dampers on cable damping considering the cable stiffness is performed. The comparative analysis of results shows that the considered parameters such as loss factor, spring factor, and installation location of dampers have much effect on the stay cables damping ratio. Finally, the optimal parameters of the two HDR dampers are proposed for damper design

Accessible Data Representation with Natural Sound

Sonification translates data into non-speech audio. Such auditory representations can make data visualization accessible to people who are blind or have low vision (BLV). This paper presents a sonification method for translating common data visualization into a blend of natural sounds. We hypothesize that people’s familiarity with sounds drawn from nature, such as birds singing in a forest, and their ability to listen to these sounds in parallel, will enable BLV users to perceive multiple data points being sonified at the same time. Informed by an extensive literature review and a preliminary study with 5 BLV participants, we designed an accessible data representation tool, Susurrus, that combines our sonification method with other accessibility features, such as keyboard interaction and text-to-speech feedback. Finally, we conducted a user study with 12 BLV participants and report the potential and application of natural sounds for sonification compared to existing sonification tools.https://doi.org/10.1145/3544548.358108

A Comparative Analysis on Suicidal Ideation Detection Using NLP, Machine, and Deep Learning

Social networks are essential resources to obtain information about people’s opinions and feelings towards various issues as they share their views with their friends and family. Suicidal ideation detection via online social network analysis has emerged as an essential research topic with significant difficulties in the fields of NLP and psychology in recent years. With the proper exploitation of the information in social media, the complicated early symptoms of suicidal ideations can be discovered and hence, it can save many lives. This study offers a comparative analysis of multiple machine learning and deep learning models to identify suicidal thoughts from the social media platform Twitter. The principal purpose of our research is to achieve better model performance than prior research works to recognize early indications with high accuracy and avoid suicide attempts. We applied text pre-processing and feature extraction approaches such as CountVectorizer and word embedding, and trained several machine learning and deep learning models for such a goal. Experiments were conducted on a dataset of 49,178 instances retrieved from live tweets by 18 suicidal and non-suicidal keywords using Python Tweepy API. Our experimental findings reveal that the RF model can achieve the highest classification score among machine learning algorithms, with an accuracy of 93% and an F1 score of 0.92. However, training the deep learning classifiers with word embedding increases the performance of ML models, where the BiLSTM model reaches an accuracy of 93.6% and a 0.93 F1 score

Effect of Metarhizium anisopliae (MetA1) on growth enhancement and antioxidative defense mechanism against Rhizoctonia root rot in okra

Rhizoctonia solani is an important necrotrophic pathogenic fungus that causes okra root disease and results in severe yield reduction. Many biocontrol agents are being studied with the intent of improving plant growth and defense systems and reducing crop loss by preventing fungal infections. Recently, a member of the Hypocrealean family, Metarhizium anisopliae, has been reported for insect pathogenicity, endophytism, plant growth promotion, and antifungal potentialities. This research investigated the role of M. anisopliae (MetA1) in growth promotion and root disease suppression in okra. The antagonism against R. solani and the plant growth promotion traits of MetA1 were tested in vitro. The effects of endophytic MetA1 on promoting plant growth and disease suppression were assessed in planta. Dual culture and cell-free culture filtrate assays showed antagonistic activity against R. solani by MetA1. Some plant growth promotion traits, such as phosphate solubilization and catalase activity were also exhibited by MetA1. Seed primed with MetA1 increased the shoot, root, leaves, chlorophyll content, and biomass content compared to control okra plants. The plants challenged with R. solani showed the highest hydrogen peroxide (H2O2) and lipid peroxidation (MDA) contents in the leaves of okra. Whereas MetA1 applied plants showed a reduction of H2O2 and MDA by 5.21 and 14.96%, respectively, under pathogen-inoculated conditions by increasing antioxidant enzyme activities, including catalase (CAT), peroxidase (POD), glutathione S-transferase (GST), and ascorbate peroxidase (APX), by 30.11, 10.19, 5.62, and 5.06%, respectively. Moreover, MetA1 increased soluble sugars, carbohydrates, proline, and secondary metabolites, viz., phenol and flavonoid contents in okra resulting in a better osmotic adjustment of diseases infecting plants. MetA1 reduced disease incidence by 58.33% at 15 DAI compared to the R. solani inoculated plant. The results revealed that MetA1 improved plant growth, elevated the plant defense system, and suppressed root diseases caused by R. solani. Thus, MetA1 was found to be an effective candidate for the biological control program