Electronically Excited States in Model Complexes of Noble Metal Clusters with Carbon Nanodots

Russian Physics Journal - Theoretical calculations of excited states in the complexes of gold and silver three-atom nanoclusters with carbon quantum nanodots are performed using the M062X...     

Separation,Preconcentration, and Determination of Lead,Cadmium, and Iron Using Cloud Point Extraction with Flame Atomic Absorption Spectrometry

Journal of Applied Spectroscopy - The concentration of heavy metals in drinking water is an important standard for water quality evaluation and water pipeline corrosion detection. This research...     

Evolutionary neighborhood discovery algorithm for agricultural routing planning in multiple fields

Annals of Operations Research - In recent years, operations research in agriculture has improved the harvested yield, reduced the cost and time required for field operations, and maintained...     

Finite-sample properties of estimators for first and second order autoregressive processes

Statistical Inference for Stochastic Processes - The class of autoregressive (AR) processes is extensively used to model temporal dependence in observed time series. Such models are easily...     

Cost-allocation problems for fuzzy agents in a fixed-tree network

Fuzzy Optimization and Decision Making - Cost-allocation problems in a fixed network are concerned with distributing the costs for use by a group of clients who cooperate in order to reduce such...     

Application of Machine Learning for Tool Condition Monitoring in Turning

The machining process is primarily used to remove material using cutting tools. Any variation in tool state affects the quality of a finished job and causes disturbances. So, a tool monitoring scheme (TMS) for categorization and supervision of failures has become the utmost priority. To respond, traditional TMS followed by the machine learning (ML) analysis is advocated in this paper. Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation. In the current study, investigation on the single point cutting tool is carried out while turning a stainless steel (SS) workpeice on the manual lathe trainer. The vibrations developed during this activity are examined for failure-free and various failure states of a tool. The statistical modeling is then incorporated to trace vital signs from vibration signals. The multiple-binary-rule-based model for categorization is designed using the decision tree. Lastly, various tree-based algorithms are used for the categorization of tool conditions. The Random Forest offered the highest classification accuracy, i.e., 92.6%.

     

Plasma Crystal in (3 + 1) Dimensions

JETP Letters - A quasi-two-dimensional plasma crystal in (3 + 1) dimensions has been experimentally observed for the first time; i.e., three spatial coordinates of each microparticles of the...     

On Laser-Induced High-Order Wave Mixing and Harmonic Generation in a Graphene Quantum Dot

JETP Letters - We present results of numerical investigations of high-order wave mixing/harmonic generation (HWM/HHG) with many-body interaction processes derived by a strong two-frequency circular...     

A new class of spherically symmetric gravitational collapse

Theoretical and Mathematical Physics - We discuss the gravitational collapse of a spherically symmetric perfect fluid distribution of uniformly contracting stars. In a uniformly contracting star,...     

Topical application of zein-silk sericin nanoparticles loaded with curcumin for improved therapy of dermatitis

Atopic dermatitis is characterized by leukocyte migration into the skin dermis and typically driven by excessive chemokine production at the site of inflammation. Conventional topical formulations such as gels, creams, and ointments are insufficient for this treatment because of low penetration of drug molecules into the targeted skin tissues. Herein, using a simple, green, sustainable strategy, we have developed novel primary zein nanoparticles embedded in curcumin (Cur) and coated with silk sericin (ZHSCs) for the topical delivery of Cur to penetrate into the dermis and exercise anti-dermatitis effects on the lesion with minimal side-effects. Transdermal delivery experiments and porcine skin fluorescence imaging indicated that ZHSCs facilitate the penetration of Cur across the epidermis layer of skin to reach deep-seated sites. Notably, ZHSCs = 1:0.25 (zein-to-silk sericin mass ratios of 1:0.25) markedly elevated the skin permeability and cumulative turnover of Cur transferred, which were provided a greater than a 3.8-fold increase relative to free Cur. The special nanoparticles of ZHS = 1:0.25 possessed the deepest localization depth and experience a transition of the particle structure and core-shell separation after penetrating into the dermis of skin. In a cell model of dermatitis induced by tumor necrosis factor α/interferon γ co-stimulation, compared with free Cur, Cur-loaded ZHS nanoparticles down-regulated the generation of inflammatory cytokines and chemokines in keratinocytes through suppression of the nuclear translocation of NF-κBp65 and hence exerted an anti-dermatitis effect. This strategy may provide new avenues and direction for the demanding issues of valid topical delivery systems.