An analytical framework for event mining in video data

With the high availability of digital video contents on the internet, users need more assistance to access digital videos. Various researches have been done about video summarization and semantic video analysis to help to satisfy these needs. These works are developing condensed versions of a full length video stream through the identification of the most important and pertinent content within the stream. Most of the existing works in these areas are mainly focused on event mining. Event mining from video streams improves the accessibility and reusability of large media collections, and it has been an active area of research with notable recent progress. Event mining includes a wide range of multimedia domains such as surveillance, meetings, broadcast, news, sports, documentary, and films, as well as personal and online media collections. Due to the variety and plenty of Event mining techniques, in this paper we suggest an analytical framework to classify event mining techniques and to evaluate them based on important functional measures. This framework could lead to empirical and technical comparison of event mining methods and development of more efficient structures at future.     

A new image encryption scheme with Feistel like structure using chaotic S-box and Rubik cube based P-box

Multimedia Tools and Applications - In this paper, a novel chaos-based dynamic encryption scheme with a permutation-substitution structure is presented. The S-boxes and P-boxes of the scheme are...     

Modeling and robust discrete LQ repetitive control of electrically driven robots

Discrete linear quadratic control has been efciently applied to linear systems as an optimal control.However,a robotic system is highly nonlinear,heavily coupled and uncertain.To overcome the problem,the robotic system can be modeled as a linear discrete-time time-varying system in performing repetitive tasks.This modeling motivates us to develop an optimal repetitive control.The contribution of this paper is twofold.For the frst time,it presents discrete linear quadratic repetitive control for electrically driven robots using the mentioned model.The proposed control approach is based on the voltage control strategy.Second,uncertainty is efectively compensated by employing a robust time-delay controller.The uncertainty can include parametric uncertainty,unmodeled dynamics and external disturbances.To highlight its ability in overcoming the uncertainty,the dynamic equation of an articulated robot is introduced and used for the simulation,modeling and control purposes.Stability analysis verifes the proposed control approach and simulation results show its efectiveness.     

Optical Properties of Self‐Assembled Cellulose Nanocrystals Films Suspended at Planar–Symmetrical Interfaces

Hierarchically structured materials comprising rod‐like, chiral, nanoparticles are commonly encountered in nature as they can form assemblies with exceptional optical and mechanical characteristics. These include cellulose nanocrystals (CNCs), which have a large potential for the fabrication of bioinspired materials mimicking those advanced properties. Fine‐tuning the optomechanical properties of assemblies obtained from CNCs hinges on the transformations from suspensions of liquid crystals to long‐range order in the dry state. So far, associated transitions have been studied using trivial interfaces such as planar substrates. Such transitions are explored as they evolve onto meshed supports. The meshed substrate offers a complex topology, as is encountered in nature, for the formation of CNCs films. The CNCs self‐assembly occurs under confinement and support of the framework bounding the mesh openings. This leads to coexisting suspended and supported nanoparticle layers exhibiting nematic and/or chiral nematic order. Optical microscopy combined with crossed polarizers indicate that the formation of the suspended films occurs via intermediate gelation or kinetic arrest of CNCs across the mesh's open areas. The formation of self‐standing, ultrathin films of CNCs with tunable optical properties, such as selective reflections in the visible range (structural color), is demonstrated by using the presented simple and scalable approach.     

Surface characterization and biological properties of regular dentin,demineralized dentin,and deproteinized dentin

Bone autografts are often used for reconstruction of bone defects; however, due to the limitations of autografts, researchers have been in search of bone substitutes. Dentin is of particular interest for this purpose due to high similarity to bone. This in vitro study sought to assess the surface characteristics and biological properties of dentin samples prepared with different treatments. This study was conducted on regular (RD), demineralized (DemD), and deproteinized (DepD) dentin samples. X-ray diffraction and Fourier transform infrared spectroscopy were used for surface characterization. Samples were immersed in simulated body fluid, and their bioactivity was evaluated under a scanning electron microscope. The methyl thiazol tetrazolium assay, scanning electron microscope analysis and quantitative real-time polymerase chain reaction were performed, respectively to assess viability/proliferation, adhesion/morphology and osteoblast differentiation of cultured human dental pulp stem cells on dentin powders. Of the three dentin samples, DepD showed the highest and RD showed the lowest rate of formation and deposition of hydroxyapatite crystals. Although, the difference in superficial apatite was not significant among samples, functional groups on the surface, however, were more distinct on DepD. At four weeks, hydroxyapatite deposits were noted as needle-shaped accumulations on DemD sample and numerous hexagonal HA deposit masses were seen, covering the surface of DepD. The methyl thiazol tetrazolium, scanning electron microscope, and quantitative real-time polymerase chain reaction analyses during the 10-day cell culture on dentin powders showed the highest cell adhesion and viability and rapid differentiation in DepD. Based on the parameters evaluated in this in vitro study, DepD showed high rate of formation/deposition of hydroxyapatite crystals and adhesion/viability/osteogenic differentiation of human dental pulp stem cells, which may support its osteoinductive/osteoconductive potential for bone regeneration.     

Biosynthesis of waste pistachio shell supported silver nanoparticles for the catalytic reduction processes

Silver nanoparticles (NPs) are immobilised on pistachio shell surface by Cichorium intybus L. leaves extract as an antioxidant media. The Fourier transform infrared spectra, X‐ray diffraction, field‐emission scanning electron microscopy equipped with energy‐dispersive X‐ray spectroscopy, and transmission electron microscope analyses confirmed the support of silver NPs on the pistachio shell (Ag NPs/pistachio shell). Ag NPs on the pistachio shell had a diameter basically in the 10–15 nm range. Reduction reactions of 4‐nitrophenol (4‐NP), and organic dyes at ambient condition were used in the investigation of the catalytic performance of the prepared catalyst. Through this research, the Ag NPs/pistachio shell shows a high activity and recyclability, and reusability without loss of its catalytic activity.Inspec keywords: transmission electron microscopy, nanoparticles, X‐ray diffraction, catalysis, nanofabrication, dyes, X‐ray chemical analysis, reduction (chemical), silver, catalysts, Fourier transform infrared spectra, field emission scanning electron microscopyOther keywords: waste pistachio shell, silver nanoparticles, catalytic reduction processes, pistachio shell surface, antioxidant media, infrared spectra, X‐ray diffraction, field‐emission scanning electron microscopy, energy‐dispersive X‐ray spectroscopy, transmission electron microscope analyses, reduction reactions, catalytic performance, catalytic activity, Cichorium intybus L. leaves extract, size 10.0 nm to 15.0 nm, Ag     

A new combination of artificial neural network and <Emphasis Type="Italic">K</Emphasis>-nearest neighbors models to predict blast-induced ground vibration and air-overpressure

Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) and K-nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp. Here, this combination is abbreviated using ANN-KNN. To indicate performance of the ANN-KNN model in predicting ground vibration and AOp, a pre-developed ANN as well as two empirical equations, presented by United States Bureau of Mines (USBM), were developed. To construct the mentioned models, maximum charge per delay (MC) and distance between blast face and monitoring station (D) were set as input parameters, whereas AOp and peak particle velocity (PPV), as a vibration index, were considered as output parameters. A database consisting of 75 datasets, obtained from the Shur river dam, Iran, was utilized to develop the mentioned models. In terms of using three performance indices, namely coefficient correlation (R ²), root mean square error and variance account for, the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations.     

Nano levels detection of beryllium by a novel beryllium PVC-based membrane sensor based on 2,3,5,6,8,9-hexahydro-1,4,7,10-benzotetra oxacyclododecine-12-carbaldehyde-12-(2,4-dinitrophenyl)hy

A novel poly(vinyl chloride)-based 2,3,5,6,8,9-hexahydro-1,4,7,10-benzotetra oxacyclododecine-12-carbaldehyde-12-(2,4-dinitrophenyl)hy (PBC) with sodium tetraphenyl borate (NaTPB) as an anion excluder, benzyl acetate (BA), acetophenon (AP) and o-nitrophenyloctyl ether (NPOE) as plasticizing solvent mediators was prepared and investigated as a beryllium selective sensor. The best performance was observed with the membrane having the PVC–NaTPB–NPOE–PBC composition 30%:3%:62%:5%, which worked well over a very wide concentration range (1.0×10⁻⁷ M to 1.0×10⁻¹ M). The sensor exhibits a Nernstian slope of 29.9 mV per decade of Be²⁺ activity. The detection limit of the sensor is 7.0×10⁻⁸ M (630 ppt). The proposed electrode shows excellent discriminating ability toward Be²⁺ ion with regard to alkali, alkaline earth, transition and heavy metal ions. It was successfully applied to the determination of beryllium in a mineral sample.     

Determination of surface-induced platelet activation by applying time-dependency dissipation factor versus frequency using quartz crystal microbalance with dissipation

Platelet adhesion and activation rates are frequently used to assess the thrombogenicity of biomaterials, which is a crucial step for the development of blood-contacting devices. Until now, electron and confocal microscopes have been used to investigate platelet activation but they failed to characterize this activation quantitatively and in real time. In order to overcome these limitations, quartz crystal microbalance with dissipation (QCM-D) was employed and an explicit time scale introduced in the dissipation versus frequency plots (Df–t) provided us with quantitative data at different stages of platelet activation. The QCM-D chips were coated with thrombogenic and non-thrombogenic model proteins to develop the methodology, further extended to investigate polymer thrombogenicity. Electron microscopy and immunofluorescence labelling were used to validate the QCM-D data and confirmed the relevance of Df–t plots to discriminate the activation rate among protein-modified surfaces. The responses showed the predominant role of surface hydrophobicity and roughness towards platelet activation and thereby towards polymer thrombogenicity. Modelling experimental data obtained with QCM-D with a Matlab code allowed us to define the rate at which mass change occurs (A/B), to obtain an A/B value for each polymer and correlate this value with polymer thrombogenicity.     

Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods

The development of reliable predictive models for individual cancer cell lines to identify an optimal cancer drug is a crucial step to accelerate personalized medicine, but vast differences in cancer cell lines and drug characteristics make it quite challenging to develop predictive models that result in high predictive power and explain the similarity of cell lines or drugs. Our study proposes a novel network-based methodology that breaks the problem into smaller, more interpretable problems to improve the predictive power of anti-cancer drug responses in cell lines. For the drug-sensitivity study, we used the GDSC database for 915 cell lines and 200 drugs. The theory of optimal mass transport was first used to separately cluster cell lines and drugs, using gene-expression profiles and extensive cheminformatic drug features, represented in a form of data networks. To predict cell-line specific drug responses, random forest regression modeling was separately performed for each cell-line drug cluster pair. Post-modeling biological analysis was further performed to identify potential biological correlates associated with drug responses. The network-based clustering method resulted in 30 distinct cell-line drug cluster pairs. Predictive modeling on each cell-line-drug cluster outperformed alternative computational methods in predicting drug responses. We found that among the four drugs top-ranked with respect to prediction performance, three targeted the PI3K/mTOR signaling pathway. Predictive modeling on clustered subsets of cell lines and drugs improved the prediction accuracy of cell-line specific drug responses. Post-modeling analysis identified plausible biological processes associated with drug responses.