Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds

We propose “supervised principal component analysis (supervised PCA)”, a generalization of PCA that is uniquely effective for regression and classification problems with high-dimensional input data. It works by estimating a sequence of principal components that have maximal dependence on the response variable. The proposed supervised PCA is solvable in closed-form, and has a dual formulation that significantly reduces the computational complexity of problems in which the number of predictors greatly exceeds the number of observations (such as DNA microarray experiments). Furthermore, we show how the algorithm can be kernelized, which makes it applicable to non-linear dimensionality reduction tasks. Experimental results on various visualization, classification and regression problems show significant improvement over other supervised approaches both in accuracy and computational efficiency.     

Measurement of the amount and rate of methane dissolution in pure water and aqueous solution of SDS + multi-wall carbon nanotubes + β-cyclodextrin

In this paper, the impact of the mixture of sodium dodecyl sulfate (SDS) + multi-wall carbon nanotubes (MWCNTs) + β-cyclodextrin on the quantity and initial rate of methane dissolved in water is investigated. The experiments were performed at a temperature range of 278.15–303.15 K and an initial pressure of 0.5 MPa. The experimental results show that simultaneous utilization of β-cyclodextrin (0.01 mass fraction), MWCNTs (0.0005 mass fraction), and SDS (0.001 mass fraction) at 278.15 K increases the amount and the rate of methane dissolution in water by 29.90% and 173.78%, respectively, compared to pure water. An increase in the temperature decreases the quantity and the initial rate of methane dissolution in all solutions containing additives. However, no consistent relationship is observed between the temperature and the enhancement percentage of solubility of methane in solutions containing additives.     

Training neural networks using Central Force Optimization and Particle Swarm Optimization: Insights and comparisons

Central Force Optimization (CFO) is a novel and upcoming metaheuristic technique that is based upon physical kinematics. It has previously been demonstrated that CFO is effective when compared with other metaheuristic techniques when applied to multiple benchmark problems and some real world applications. This work applies the CFO algorithm to training neural networks for data classification. As a proof of concept, the CFO algorithm is first applied to train a basic neural network that represents the logical XOR function. This work is then extended to train two different neural networks in order to properly classify members of the Iris data set. These results are compared and contrasted to results gathered using Particle Swarm Optimization (PSO) in the same applications. Similarities and differences between CFO and PSO are also explored in the areas of algorithm design, computational complexity, and natural basis. The paper concludes that CFO is a novel and promising meta-heuristic that is competitive with if not superior to the PSO algorithm, and there is much room to further improve it.     

Current and Temperature Dependent Matching Fields in Nb Film of Rectangular Array of Holes

Nanoengineered periodic array of holes on superconducting thin films have a great interest due to their excellence for the studies of the vortex pinning mechanisms in the type-II superconductors. Rectangular array of holes has been fabricated over a microbridge of Nb superconducting thin film by e-beam lithography. Rectangular array of holes have two type of scenario, at low magnetic fields matching effects are sharp and narrow while at high fields wide and shallower. In this work, we study the matching pinning effect by the artificial hole array in superconducting Nb thin films. We observed that as the inter distance between holes is decreased; the sharp matching effects become dominant and vice versa.     

Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system

Despite the proven advantages of sampling-based motion planning algorithms, their inability to handle online navigation tasks and providing low-cost solutions make them less efficient in practice. In this paper, a novel sampling-based algorithm is proposed which is able to plan in an unknown environment and provides solutions with lower cost in terms of path length, runtime and stability of the results. First, a fuzzy controller is designed which incorporates the heuristic rules of Tabu search to enable the planner for solving online navigation tasks. Then, an adaptive neuro-fuzzy inference system (ANFIS) is proposed such that it constructs and optimizes the fuzzy controller based on a set of given input/output data. Furthermore, a heuristic dataset generator is implemented to provide enough data for the ANFIS using a randomized procedure. The performance of the proposed algorithm is evaluated through simulation in different motion planning queries. Finally, the proposed planner is compared to some of the similar motion planning algorithms to support the claim of superiority of its performance.

     

Induction time,storage capacity,and rate of methane hydrate formation in the presence of SDS and silver nanoparticles

In this communication, the kinetic parameters of methane hydrate formation (induction time, quantity and rate of gas uptake, storage capacity (SC), and apparent rate constant) in the presence of sodium dodecyl sulfate (SDS), synthetized silver nanoparticles (SNPs), and mixture of SDS?+?SNPs have been studied. Experimental measurements were performed at temperature of 273.65?K and initial pressure of 7?MPa in a 460?cm³ stirred batch reactor. Our results show that adding SDS, SNPs and their mixture increases the quantity of gas uptake, water to hydrate conversion, and SC of methane hydrate formation, noticeably. Using 300?ppm SDS increases the SC and the quantity of methane uptake 615, and 770%, respectively, compared with pure water. Investigating the hydrate growth rate at the start of hydrate formation process shows that, using SNPs, SDS, and their mixture increases the initial apparent rate constant of hydrate rate, considerably. Our results show that the system of methane?+?water?+?SDS 500?ppm?+?SNPs 45?µM represents the maximum value of initial apparent rate constant, compared with other tested systems.     

Development and Validation of an Artificial Mechanical Skin Model for the Study of Interactions between Skin and Microneedles

Microneedles are small needle‐like structures that are almost invisible to the naked eye. They have an immense potential to serve as a valuable tool in many medical applications, such as painless vaccination. Microneedles work by breaking through the stratum corneum, the outermost barrier layer of the skin, and providing a direct path for drug delivery into the skin. A lot of research has been presented over the past two decades on the applications of microneedles, yet the fundamental mechanism of how they interact, pressure, and penetrate the skin in its native state is worth examining further. As such, a major difficulty with understanding the mechanism of microneedle–skin interaction is the lack of an artificial mechanical human skin model to use as a standardized substrate. In this research news, the development of an artificial mechanical skin model based on a thorough mechanical study of fresh human and porcine skin samples is presented. The artificial mechanical skin model can be used to study the mechanical interactions between microneedles and skin, but not diffusion of molecules across skin. This model can assist in improving the performance of microneedles by enhancing the reproducibility of microneedle depth insertions for optimal drug delivery and biosensing.

     

Polymeric Nanoparticle-Based Photodynamic Therapy for Chronic Periodontitis in Vivo

Antimicrobial photodynamic therapy (aPDT) is increasingly being explored for treatment of periodontitis. Here, we investigated the effect of aPDT on human dental plaque bacteria in suspensions and biofilms in vitro using methylene blue (MB)-loaded poly(lactic-co-glycolic) (PLGA) nanoparticles (MB-NP) and red light at 660 nm. The effect of MB-NP-based aPDT was also evaluated in a clinical pilot study with 10 adult human subjects with chronic periodontitis. Dental plaque samples from human subjects were exposed to aPDT—in planktonic and biofilm phases—with MB or MB-NP (25 µg/mL) at 20 J/cm² in vitro. Patients were treated either with ultrasonic scaling and scaling and root planing (US + SRP) or ultrasonic scaling + SRP + aPDT with MB-NP (25 µg/mL and 20 J/cm²) in a split-mouth design. In biofilms, MB-NP eliminated approximately 25% more bacteria than free MB. The clinical study demonstrated the safety of aPDT. Both groups showed similar improvements of clinical parameters one month following treatments. However, at three months ultrasonic SRP + aPDT showed a greater effect (28.82%) on gingival bleeding index (GBI) compared to ultrasonic SRP. The utilization of PLGA nanoparticles encapsulated with MB may be a promising adjunct in antimicrobial periodontal treatment.     

Thermal Upgrading of Nickeliferous Pyrrhotite Tailings for the Recovery of Nickel in the Form of Ferronickel Alloy

Metallurgical and Materials Transactions B - Production of ferronickel alloy by thermal treatment of nickeliferous pyrrhotite (Pyrr) tailings was studied by both thermodynamic assessment and...