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An abundance of biomedical data is generated in the form of clinical notes, reports, and research articles available online. This data holds valuable information that requires extraction, retrieval, and transformation into actionable knowledge. However, this information has various access challenges due to the need for precise machine-interpretable semantic metadata required by search engines. Despite search engines' efforts to interpret the semantics information, they still struggle to index, search, and retrieve relevant information accurately. To address these challenges, we propose a novel graph-based semantic knowledge-sharing approach to enhance the quality of biomedical semantic annotation by engaging biomedical domain experts. In this approach, entities in the knowledge-sharing environment are interlinked and play critical roles. Authorial queries can be posted on the "Knowledge Cafe," and community experts can provide recommendations for semantic annotations. The community can further validate and evaluate the expert responses through a voting scheme resulting in a transformed "Knowledge Cafe" environment that functions as a knowledge graph with semantically linked entities. We evaluated the proposed approach through a series of scenarios, resulting in precision, recall, F1-score, and accuracy assessment matrices. Our results showed an acceptable level of accuracy at approximately 90%. The source code for "Semantically" is freely available at: https://github.com/bukharilab/Semantically 

The shape of a foil undergoing a combined pitching heaving motion is critical to its design in applications that demand high efficiency and thrust. This study focuses on understanding of how the shape of a foil affects its propulsive performance. We perform two-dimensional numerical simulations of fluid flows around a flapping foil for different governing parameters in the range of biological swimmers and bio-inspired underwater vehicles. By varying the foil shape using a class-shape transformation method, we investigate a broad range of foil-like shapes. In the study, we also show consistent results with previous studies that a thicker leading-edge and sharper trailing-edge makes for a more efficient foil shape undergoing a flapping motion. In addition, we explain that the performance of the foil is highly sensitive to its shape, specifically the thickness of the foil between the 18th and 50th percent along the chord of the foil. Moreover, we elucidate the flow mechanisms behind variations in performance metrics, particularly focused on constructive interference between the vortices generated at the leading-edge with the trailing-edge vortex, as well as the pressure field differences that lead to higher power consumption in less efficient foil shapes. 

This work presents a control scheme for wind-induced vibration mitigation for tall buildings based on a gated recurrent unit (GRU) encoder-decoder model which operates using readings from multiple sensors to define a unique system state. The sensors include a distributed network of pressure probes installed on surrounding buildings, accelerometers installed on the principal building, and atmospheric conditions. The encoder-decoder GRU is trained from timeseries sensor readings to construct a unique internal representation (hidden state) of the evolving wind and building conditions. A 1:400-scale aeroelastic building model with motorized plates acting as aerodynamic control surfaces is used in wind tunnel experiments to conduct this study. An online genetic reinforcement learning (GRL) algorithm uses a series of multilayer perceptron (MLP) networks to determine optimum actuator orientations for different flow conditions. The algorithm stores previously discovered solutions in the MLPs sorted by their fitness. The GA operates by obtaining a solution from each of the MLPs and performing GA operations on them to choose the next combination of plate angles to try. A chance also exists for trying completely random plate angles to prevent the GA from stalling. The MLPs are continuously trained during online optimization using findings obtained from new trials. The system eliminates the need for holding wind conditions, which are uncontrollable, constant during online training but still uses a pseudo-random search technique to obtain global optimum solutions. Results show a considerable reduction in building RMS acceleration when compared with a large collection of results with random constant plate angle orientations. 

Tall and slender buildings often endure disturbances resulting from winds composed of various mean and fluctuating velocities. These disturbances result in discomfort for the occupants as well as accelerated fatigue life cycles and premature fatigue failures in the building. This work presents the development of a smart morphing façade (Smorphaçade) system that dynamically alters a buildings’ external shape or texture to minimize the effect of wind-induced vibrations on the building. The Smorphacade system is represented in this work by a series of plates that vary their orientation by means of a central controller module. To validate the simulation, a simple NACA0012 airfoil is simulated in a stream of air at a Reynolds number (RE) of 2 million. The pressure and viscous force profiles are captured to plot the variation of the lift force for different angles of attack that are then validated using published experimental airfoil data. After validation, the airfoil is attached to a linear spring-damper combination and is allowed to translate vertically without rotation according to the force profile captured from the surrounding air stream. A PID controller is developed to equilibrate the vertical position of the airfoil by altering its angle of attack. The model and its utility functions are implemented as an OpenFOAM® module (MSLSolid). Thereafter, the model is expanded to handle a planar case of a building floor carrying 4 controllable plates. The forces on the building profile are summed at the centroid of the building and the windward rigid body motion of the floor is estimated by reflecting the horizontal force component on a Finite Element (FE) model of the building. The time series information of the force acting on the building and the resulting oscillations are captured for exhaustive combinations of the plate angles. This data is used to build a lookup table that gives the best plate configuration for a given wind condition. A controller operates in real-time by searching the lookup table using readings of the wind condition. Preliminary results show a 94% reduction in the amplitudes of wind-induced vibrations. 

Background: Estrogen Receptors (ER) are members of the nuclear intracellular receptorsfamily. ER once activated by estrogen, it binds to DNA via translocating into the nucleus and regulatesthe activity of various genes. Withaferin A (WA) - an active compound of a medicinal plant Withaniasomnifera was reported to be a very effective anti-cancer agent and some of the recent studies hasdemonstrated that WA is capable of arresting the development of breast cancer via targeting estrogenreceptor. Objective: The present study is aimed at understanding the molecular level interactions of ER and Tamoxifenin comparison to Withaferin A using In-silico approaches with emphasis on Withaferin Abinding capability with ER in presence of point mutations which are causing de novo drug resistance toexisting drugs like Tamoxifen. Methods: Molecular modeling and docking studies were performed for the Tamoxifen and WithaferinA with the Estrogen receptor. Molecular docking simulations of estrogen receptor in complex withTamoxifen and Withaferin A were also performed. Results: Amino acid residues, Glu353, Arg394 and Leu387 was observed as crucial for binding andstabilizing the protein-ligand complex in case of Tamoxifen and Withaferin-A. The potential ofWithaferin A to overcome the drug resistance caused by the mutations in estrogen receptor to the existingdrugs such as Tamoxifen was demonstrated. Conclusion: In-silico analysis has elucidated the binding mode and molecular level interactions whichare expected to be of great help in further optimizing Withaferin A or design / discovery of futurebreast cancer inhibitors targeting estrogen receptor.