The effects of periodontal therapy on serum antibody levels to Actinobacillus actinomycetemcomitans and Porphyromonas gingivalis (part II)

Levels of IgG and IgM antibodies were estimated against Actinobacillus actinomycetemcomitans and Porphyromonas gingivalis were determined by enzyme linked immunosorbent assay (ELISA) in 17 patients with juvenile periodontitis, 15 with adult periodontitis and 24 healthy controls at the beginning of treatment and 3 to 8 months after periodontal therapy. After treatment, antibodies to A. actinomycetemcomitans and P.gingivalis had decreased in patients, but the levels were still significantly higher than in healthy controls. Whether or not an of antibody level against a specific bacteria changes after periodontal treatment is however, still debatable.     

Dynamical system parameter identification using deep recurrent cell networks

Neural Computing and Applications - In this paper, we investigate the parameter identification problem in dynamical systems through a deep learning approach. Focusing mainly on second-order, linear...     

Fine grinding of thermoplastics by high speed friction grinding assisted by guar gum

High speed friction grinding has been used to grind plant and food substances in water but never been explored for grinding of thermoplastics like polylactic acid (PLA), low and high density polyethylene and polypropylene. Such grinding was investigated in this work and was made possible by using 0.5% guar gum solution instead of just water because increasing the viscosity of water reduced their settling and the speed of passing through the grinder. Tensile, flexural, and impact strengths of the plastics were studied and higher grinding efficiency of PLA could be explained by its low elongation-at-break compared to low density polyethylene, high density polyethylene, and polypropylene. The microplastics (2000–45 μm) were studied for mass and particle size distributions and by scanning electron microscopy, ¹³C CP/MAS NMR, Fourier transform infrared spectroscopy, differential scanning calorimetry, and thermogravimetric analysis. In addition, viscosity of guar gum and contact angles was measured. This new technology can produce finely ground microplastics (710–45 μm) for a variety of applications.     

The Training of Pi-Sigma Artificial Neural Networks with Differential Evolution Algorithm for Forecasting

Computational Economics - Looking at the artificial neural networks’ literature, most of the studies started with feedforward artificial neural networks and the training of many feedforward...     

Resistorless floating immittance function simulators employing current controlled conveyors and a grounded capacitor

In this paper, new floating immittance function simulators employing second-generation current controlled conveyors are proposed. The first four of the presented circuits employ only a single grounded capacitor as passive component and can realize either a negative or a positive floating inductor or capacitor. The last two of the proposed circuits do not employ passive components and can realize either negative or positive floating resistances. All of the proposed circuits do not require passive element matching. As an application, a third-order butterworth filter is realized using the proposed positive floating inductance simulator. SPICE simulation results and large signal behavior of the filter are included.     

Parallel-machine rescheduling with machine disruptions

In this study we consider a rescheduling problem on identical parallel machines. The rescheduling is undertaken because of a period of unavailability on one of the machines. We consider the total flow time as an efficiency measure and stability is gauged in terms of the number of jobs processed on different machines in the original and new schedules. We show that all efficient schedules with respect to efficiency and stability measures can be generated in polynomial time.     

Towards quality requirements via animated formal specifications

Assuring a high quality requirements specification document involves both an early validation process and an increased level of participation. An approach and its supporting environment which combines the benefits of a formal system specification and its subsequent execution via a rapid prototype is reported. The environment assists in the construction, clarification, validation and visualisation of a formal specification. An illustrative case study demonstrates the consequences of assertions about system properties at this early stage of software development. Our approach involves the pragmatic combination of technical benefits of formal systems engineering based techniques with the context‐sensitive notions of increased participation of both developer and user stakeholders to move us closer towards a quality requirements specification document.     

Ensemble learning of diffractive optical networks

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning.Specifically,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation speed.Diffractive deep neural networks(D²NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D²NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and imaging.Here,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble learning.After independently training 1252 D²NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D²NNs that collectively improved the image classification accuracy.Through this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D²NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D²NNs within each ensemble.These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems.