Computational fluid dynamics model of a quad-rotor helicopter for dynamic analysis

The control and performance of a quad-rotor helicopter UAV is greatly influenced by its aerodynamics, which in turn is affected by the interactions with features in its remote environment. This paper presents details of Computational Fluid Dynamics (CFD) simulation and analysis of a quadrotor helicopter. It starts by presenting how SolidWorks software is used to develop a 3-D Computer Aided Design (CAD) model of the quad-rotor helicopter, then describes how CFD is used as a computer based mathematical modelling tool to simulate and analyze the effects of wind flow patterns on the performance and control of the quadrotor helicopter. For the purpose of developing a robust adaptive controller for the quad-rotor helicopter to withstand any environmental constraints, which is not within the scope of this paper; this work accurately models the quad-rotor static and dynamic characteristics from a limited number of time-accurate CFD simulations

Modelling and simulation of a quad-rotor helicopter

Small size quad-rotor helicopters are often used due to the simplicity of their construction and maintenance, their ability to hover and also to take-off and land vertically. The first step in control development is an adequate dynamic system modelling, which should involve a faithful mathematical representation of the mechanical system. This paper presents a detailed dynamic analytical model of the quad-rotor helicopter using the linear Taylor series approximation method. The developed analytical model was simulated in the MatLab/Simulink environment and the dynamic behaviour of the quad-rotor assessed due to voltage changes. The model is further calibrated and linearized for use on any quad-rotor helicopter

Mechanism of the Anti-inflammatory Effect of Curcumin: PPAR-γ Activation

Curcumin, the phytochemical component in turmeric, is used as a dietary spice and a topical ointment for the treatment of inflammation in India for centuries. Curcumin (diferuloylmethane) is relatively insoluble in water, but dissolves in acetone, dimethylsulphoxide, and ethanol. Commercial grade curcumin contains 10–20% curcuminoids, desmethoxycurcumin, and bisdesmethoxycurcumin and they are as effective as pure curcumin. Based on a number of clinical studies in carcinogenesis, a daily oral dose of 3.6 g curcumin has been efficacious for colorectal cancer and advocates its advancement into Phase II clinical studies. In addition to the anticancer effects, curcumin has been effective against a variety of disease conditions in both in vitro and in vivo preclinical studies. The present review highlights the importance of curcumin as an anti-inflammatory agent and suggests that the beneficial effect of curcumin is mediated by the upregulation of peroxisome proliferator-activated receptor-γ (PPAR-γ) activation

The Application of Liquid Junctions for Characterization of Semiconductor Materials

In this study, liquid junctions were used to characterize silicon and silicon subjected to various reactive ion etching (RIE); surface optimization of CuInSe{dollar}\sb2{dollar}; and photo-modification of InSe. Impedance spectroscopy and modulation spectroscopies such as electrolyte electroreflectance (EER), photoreflectance (PR), and photoreflectance in the presence of electrolyte (EPR) were the major methodologies that were used for investigating the dielectric properties of the semiconductors and their interface with the ambients. It was shown that the above experimental techniques provide information about the flat-band potential, doping density, Fermi level pinning, the density and distribution of surface states, energy gap and broadening parameter related to the lifetime of majority carriers, etc. The effective medium analysis of the frequency dispersion of the impedance provides the information on the microstructure of the composite at the interface. The analysis of the constant phase angle (CPA) elements reveals the origin of disorder such as diffusion of minority carriers. The change of the line shape of the modulation spectrum provides a sensitive probe for analyzing the tensile strain, the quality of the crystal, etc. Both techniques can be complementary and cross-checked, which comprise a versatile system of characterization for the dielectric properties of semiconducting materials

Classification of integers based on residue classes via modern deep learning algorithms

Judging whether an integer can be divided by prime numbers such as 2 or 3 may appear trivial to human beings, but can be less straightforward for computers. Here, we tested multiple deep learning architectures and feature engineering approaches on classifying integers based on their residues when divided by small prime numbers. We found that the ability of classification critically depends on the feature space. We also evaluated Automated Machine Learning (AutoML) platforms from Amazon, Google and Microsoft, and found that they failed on this task without appropriately engineered features. Furthermore, we introduced a method that utilizes linear regression on Fourier series basis vectors, and demonstrated its effectiveness. Finally, we evaluated Large Language Models (LLMs) such as GPT-4, GPT-J, LLaMA and Falcon, and demonstrated their failures. In conclusion, feature engineering remains an important task to improve performance and increase interpretability of machine-learning models, even in the era of AutoML and LLMs.Comment: Accepted at Pattern

Prediction of Yield Surface of Single Crystal Copper from Discrete Dislocation Dynamics and Geometric Learning

A yield surface of a material is a set of critical stress conditions beyond which macroscopic plastic deformation begins. For crystalline solids, plastic deformation occurs by the motion of dislocations, which can be captured by discrete dislocation dynamics (DDD) simulations. In this paper, we predict the yield surfaces and strain-hardening behaviors using DDD simulations and a geometric manifold learning approach. The yield surfaces in the three-dimensional space of plane stress are constructed for single-crystal copper subjected to uniaxial loading along the $[100]$ and $[110]$ directions, respectively. With increasing plastic deformation under $[100]$ loading, the yield surface expands nearly uniformly in all directions, corresponding to isotropic hardening. In contrast, under $[110]$ loading, latent hardening is observed, where the yield surface remains nearly unchanged in the orientations in the vicinity of the loading direction itself, but expands in other directions, resulting in an asymmetric shape. This difference in hardening behaviors is attributed to the different dislocation multiplication behaviors on various slip systems under the two loading conditions

HIV-1 TAT-mediated protein transduction and subcellular localization using novel expression vectors11The nucleotide sequences of vectors pETAT-1/2/11/12, pNB-3/13, pHis-TAT-GFP, pHis-TAT-m-GFP and pHis-GFP have been deposited in GenBank under accession numbers AF525441–525449.

AbstractSeveral novel prokaryotic and eukaryotic expression vectors were constructed for protein transduction and subcellular localization. These vectors employed an N-terminal stretch of 11 basic amino acid residues (47–57) from the human immunodeficiency virus type 1 (HIV-1) TAT protein transduction domain (PTD) for protein translocation and cellular localization. The vectors also contained a six-histidine (His6) tag at the N- or C-terminus for convenient purification and detection, and a multiple cloning site for easy insertion of foreign genes. Some heterologous genes including HSV-TK, Bcl-rambo, Smac/DIABLO and GFP were fused in-frame to TAT PTD and successfully overexpressed in Escherichia coli. The purified TAT-GFP fusion protein was able to transduce into the mammalian cells and was found to locate mainly in the cytosol when exogenously added to the cell culture medium. However, using a transfection system, mammalian-expressed TAT-GFP predominantly displayed a nuclear localization and nucleolar accumulation in mammalian cell lines. This discrepancy implies that the exact subcellular localization of transduced protein may depend on cell type, the nature of imported proteins and delivery approach. Taken together, our results demonstrate that a TAT PTD length of 11 amino acids was sufficient to confer protein internalization and its subsequent cellular localization. These novel properties allow these vectors to be useful for studying protein transduction and nuclear import

Small molecule-mediated tribbles homolog 3 promotes bone formation induced by bone morphogenetic protein-2.

Although bone morphogenetic protein-2 (BMP2) has demonstrated extraordinary potential in bone formation, its clinical applications require supraphysiological milligram-level doses that increase postoperative inflammation and inappropriate adipogenesis, resulting in well-documented life-threatening cervical swelling and cyst-like bone formation. Recent promising alternative biomolecular strategies are toward promoting pro-osteogenic activity of BMP2 while simultaneously suppressing its adverse effects. Here, we demonstrated that small molecular phenamil synergized osteogenesis and bone formation with BMP2 in a rat critical size mandibular defect model. Moreover, we successfully elicited the BMP2 adverse outcomes (i.e. adipogenesis and inflammation) in the mandibular defect by applying high dose BMP2. Phenamil treatment significantly improves the quality of newly formed bone by inhibiting BMP2 induced fatty cyst-like structure and inflammatory soft-tissue swelling. The observed positive phenamil effects were associated with upregulation of tribbles homolog 3 (Trib3) that suppressed adipogenic differentiation and inflammatory responses by negatively regulating PPARγ and NF-κB transcriptional activities. Thus, use of BMP2 along with phenamil stimulation or Trib3 augmentation may be a promising strategy to improve clinical efficacy and safety of current BMP therapeutics

Quantum-Inspired Machine Learning: a Survey

Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within classical computational frameworks. However, current review literature often presents a superficial exploration of QiML, focusing instead on the broader Quantum Machine Learning (QML) field. In response to this gap, this survey provides an integrated and comprehensive examination of QiML, exploring QiML's diverse research domains including tensor network simulations, dequantized algorithms, and others, showcasing recent advancements, practical applications, and illuminating potential future research avenues. Further, a concrete definition of QiML is established by analyzing various prior interpretations of the term and their inherent ambiguities. As QiML continues to evolve, we anticipate a wealth of future developments drawing from quantum mechanics, quantum computing, and classical machine learning, enriching the field further. This survey serves as a guide for researchers and practitioners alike, providing a holistic understanding of QiML's current landscape and future directions.Comment: 56 pages, 13 figures, 8 table