Synthesis of polypropylene/cellulosic fiber composites using Ziegler‐Natta catalyst by in situ polymerization

Composites of polypropylene/natural fiber were obtained synthesizing polypropylene in the presence of chemically treated fibers. For this, vegetal cellulose fibers were introduced in the polymerization medium of propylene using Ziegler‐Natta catalyst. The fibers were treated to increase their compatibility with the polypropylene matrix and decrease the accessibility of functional groups to the catalyst active sites. At first, mild acid hydrolysis was performed, followed by chemical treatments with triethylaluminum and stearic alcohol. The results showed that the chemical treatments increased the crystallinity index to 73–77% in comparison with the original fiber (57.6%). The catalyst activity and its stereospecificity maintained high when fiber was treated with triethylaluminium and that with stearic alcohol. The composites contained amounts of fiber in a range of 3.1–5.8%. The fibers did not induce crystallization of PP crystallites different from the most common α‐form. Fibers were fibrillated to nanometric diameter, although micrometric length. POLYM. ENG. SCI., 56:71–78, 2016. © 2015 Society of Plastics Engineers     

Analysis of Pu, Cm and Am alpha spectra using the unfolding code Colégram without prior use of a nuclear data library

This paper shows the unfolding capabilities of a code developed in our laboratory when applied to alpha spectrometry. The code, called ‘Colégram’, uses the Levenberg–Marquardt algorithm. Colégram has some particularly interesting features when compared to other codes using χ² as the convergence criterion. Low statistics peaks are treated more successfully. No nuclear data library is needed. The emission probabilities obtained with Colégram from ²³⁹Pu, ²⁴⁴Cm and ²⁴³Am alpha spectra characterized by high statistics and good energy resolution are presented. These emission probabilities and the alpha-particle energies deduced from them are compared to those obtained in the most recent works. We show that Colégram also offers the possibility of identifying and quantifying contaminants present in the spectra. Finally, with Colégram, it is necessary to identify electron conversion lines and include them in the unfolding process to increase the quality of the fits.     

Geometrical codification for clustering mixed categorical and numerical databases

This paper presents an alternative to cluster mixed databases. The main idea is to propose a general method to cluster mixed data sets, which is not very complex and still can reach similar levels of performance of some good algorithms. The proposed approach is based on codifying the categorical attributes and use a numerical clustering algorithm on the resulting database. The codification proposed is based on polar or spherical coordinates, it is easy to understand and to apply, the increment in the length of the input matrix is not excessively large, and the codification error can be determined for each case. The proposed codification combined with the well known k-means algorithm showed a very good performance in different benchmarks and has been compared with both, other codifications and other mixed clustering algorithms, showing a better or comparable performance in all cases.     

Enzyme‐Mediated Tumor Starvation and Phototherapy Enhance Mild‐Temperature Photothermal Therapy

Compared with conventional tumor photothermal therapy (PTT), mild‐temperature PTT brings less damage to normal tissues, but also tumor thermoresistance, introduced by the overexpressed heat shock protein (HSP). A high dose of HSP inhibitor during mild‐temperature PTT might lead to toxic side effects. Glucose oxidase (GOx) consumes glucose, leading to adenosine triphosphate supply restriction and consequent HSP inhibition. Therefore, a combinational use of an HSP inhibitor and GOx not only enhances mild‐temperature PTT but also minimizes the toxicity of the inhibitor. However, a GOx and HSP inhibitor‐encapsulating nanostructure, designed for enhancing its mild‐temperature tumor PTT efficiency, has not been reported. Thermosensitive GOx/indocyanine green/gambogic acid (GA) liposomes (GOIGLs) are reported to enhance the efficiency of mild‐temperature PTT of tumors via synergistic inhibition of tumor HSP by the released GA and GOx, together with another enzyme‐enhanced phototherapy effect. In vitro and in vivo results indicate that this strategy of tumor starvation and phototherapy significantly enhances mild‐temperature tumor PTT efficiency. This strategy could inspire people to design more delicate platforms combining mild‐temperature PTT with other therapeutic methods for more efficient cancer treatment.     

Certain antioxidant enzymes of Allium cepa as biomarkers for the detection of toxic heavy metals in wastewater

The utility of antioxidant enzymes, viz glutathione-S-transferase (GST), glutathione reductase (GR), superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), glutathione peroxidase (GPX), monodehydroascorbate reductase (MDHAR) and dehydroascorbate reductase (DHAR), as biomarkers of heavy metal pollution in water was investigated using the Allium cepa (onion) system. These antioxidant enzymes were assayed in onion bulbs exposed to certain heavy metals taken separately, the test metals taken in combination as well as the industrial wastewater especially found to contain these metals. GST exhibited significantly enhanced activity upon treatment with individual heavy metals. However, GR, SOD and CAT did not show such a pronounced increase in activities. At higher heavy metal concentrations, GR, SOD and CAT showed a steep decline while GST activity still showed a rise. Moreover, APX, GPX and MDHAR also exhibited remarkable induction with increase in the concentration of individual heavy metals. However, there was no significant change in DHAR activity with respect to the controls. Metabolites like ascorbate (ASC) and glutathione (GSH) exhibited significant decline with increase in the concentration of individual heavy metals while the level of H(2)O(2) continued to display the rise up to a heavy metal concentration of 100 microM, after which it showed a gradual decline. A. cepa bulbs treated with wastewater sample showed enzyme activity profiles similar to that shown with heavy metals, thereby suggesting the presence of heavy metals in the test wastewater. Atomic absorption spectrophotometry also detected large amounts of Cd, Cr, Cu, Hg, Pb and Zn in the test water sample. The metal mixture, containing the amounts of heavy metals equivalent to those found in the wastewater, resulted in steep declines in GR, SOD and CAT activities in A. cepa while GST showed a rise. However, when this metal mixture was diluted to 2000-fold, GR, SOD and CAT also showed enhanced activities compared with the controls. Contrary to the above finding, APX, GPX and MDHAR exhibited the rise in activities in A. cepa exposed to the metal mixture at all dilutions. In the presence of cycloheximide, all the enzymes returned to the levels of untreated controls while chloramphenicol did not have any effect on the test enzymes, thereby suggesting de novo protein synthesis of the test antioxidant enzymes in the cytosolic compartment of the cell as a result of exposure to the heavy metals.     

Spin State of Cobalt and Electrical Transport Mechanism in MgCo<Subscript>2</Subscript>O<Subscript>4</Subscript> System

MgCo₂O₄ samples were synthesized by inverse co-precipitation method. The formation of a single-phase spinel structure was confirmed by X-ray diffraction measurements and Fourier-transform infrared spectroscopy. The samples crystallized in a face-centered cubic structure with Fd-3m space group as revealed from the Rietveld refinement of X-ray diffraction data. Magnetic measurements carried out in a broad temperature range of 5–300 K showed antiferromagnetic to paramagnetic phase transition (Neel temperature) observed at 101 K. Magnetic susceptibility data fitted using the Curie Weiss law and effective Bohr magnetic moment (μ_eff) for Co atoms was determined. Calculated μ_eff comes out to be 3.05 μ_B. These results were correlated to the spin states of Co³⁺ atoms. A small hysteresis in the field-dependent magnetization MH loop taken at 5 K indicates the existence of weak ferromagnetism in this system. The electrical resistivity measurement in the temperature range 77–750 K displayed the semiconducting-like behavior for this system.     

Synergies between texture features: an abstract feature based framework for meningioma subtypes classification

Histopathology is the gold standard for accurate diagnosis of cancer, tumors and similar diseases. Real-world pathological images, due to non-homogeneous nature and unorganized spatial intensity variations, are complex to analyze and classify. The major challenge in classifying pathological images is the complexity due to high intra-class variability and low inter-class variation in texture. Accuracy of histopathological image classification is highly dependent on the relevancy of the selected features to the problem. This paper is an effort in the same direction and presents an abstract feature based framework called abstract feature framework (AFF) to select optimal set of the most relevant features to classify pathological images. An abstract feature is created by identifying interlinked run-length texture features and grouping them. AFF is comprised of a new data structure called Abstract Feature Tree (AFT) and an algorithm for manipulating it. AFT is a tree structure in which nodes are abstract features. The Linkage Learning Algorithm for manipulating AFT is the brain of this framework and inspired by genetic algorithm. It creates better abstract features by first identifying interlinked abstract features and then combining them. This process is repeated until no improvement is found. On termination, the final list of abstract features is used for classifying pathological images. The proposed framework was tested on real-world histopathological meningioma dataset. Results obtained proved that the proposed framework outperformed the best-known rank-based feature selection techniques by using, on average, approximately three times less features to achieve 22% higher classification accuracy.     

Muscle circumference sensor and model reference-based adaptive impedance control for upper limb assist exoskeleton robot

In this paper, we have addressed two issues for upper limb assist exoskeleton: (1) estimation of human desired motion intention (DMI) using non-biological-based sensors; and (2) compliant control using model reference-based adaptive approach. For non-biological-based DMI estimation, we have employed Muscle Circumference Sensor (MCS) and load cells. MCS measures human elbow joint torque using human arm kinematics, biceps/triceps muscle model, and physiological cross-sectional area of these muscles. So, using MCS, we have measured Biceps/Triceps internal muscle activity and we have tried to reduce it by providing robotic assistance. To extract DMI, we have employed radial basis function neural network (RBFNN). RBFNN uses position, velocity, and human force to estimate DMI which is further tracked by the impedance control law. This algorithm is based on model reference-based adaptive impedance control law which drives the overall assist exoskeleton to the desired reference impedance model, giving required compliance. To highlight the effectiveness, we have compared proposed control algorithm with simple impedance and adaptive impedance control algorithms. Experimental results demonstrate the reduced muscle activity and active compliance for subject wearing the robot.     

Consensus for linear multi-agent system with intermittent information transmissions using the time-scale theory

This paper investigates the consensus problem for linear multi-agent system with fixed communication topology in the presence of intermittent communication using the time-scale theory. Since each agent can only obtain relative local information intermittently, the proposed consensus algorithm is based on a discontinuous local interaction rule. The interaction among agents happens at a disjoint set of continuous-time intervals. The closed-loop multi-agent system can be represented using mixed linear continuous-time and linear discrete-time models due to intermittent information transmissions. The time-scale theory provides a powerful tool to combine continuous-time and discrete-time cases and study the consensus protocol under a unified framework. Using this theory, some conditions are derived to achieve exponential consensus under intermittent information transmissions. Simulations are performed to validate the theoretical results.