Acetals as nucleating agents for polypropylene

Nucleating agents increase the impact strength, tensile strength, and tensile elasticity modulus of semicrystalline polymers. Nucleating agents also decrease product cycle times, resulting in a cost savings per product unit. We have synthesized and tested 15 compounds as nucleactors for polypropylene. Of these, trinapthylidene sorbitol, tri-(4-methyl-1-naphthylidene)sorbitol, tri-(4-methoxy-1-naphthylidene) sorbitol, and dibenzylidene xylitol are efficient nucleators of polypropylene. Trinaphthylidene sorbitol (tns) has two major diastereomers: The “S” diastereomer yields a faster crystallization rate for polypropylene than does the commercial nucleator dibenzylidene sorbitol (Millad 3905). Crystallization rates are 208 and 88, respectively (t min^?1 × 1000). The “R” diastereomer, however, is a poor nucleator and interferes with the nucleating activity of the “S” diastereomer. A 52/48 mixture of diastereomers does not nucleate polypropylene, even at twice the concentration. This is first time that the importance of stereochemistry has been demonstrated in the nucleating action. © 1994 John Wiley & Sons, Inc.     

Bayesian Networks for Knowledge-Based Authentication

We consider adaptive index utilization as a fine-grained problem in autonomic databases in which an existing index is dynamically determined to be used or not in query processing. As a special case, we study this problem for structural joins, the core operator in XML query processing, in the main memory. We find that index utilization is beneficial for structural joins only under certain join selectivity and distribution of matching elements. Therefore, we propose adaptive algorithms to decide whether to use an index probe or a data scan for each step of matching during the processing of a structural join operator. Our adaptive algorithms are based on the history, the look-ahead information, or both. We have developed a cost model to facilitate this adaptation and have conducted experiments with both synthetic and real-world data sets. Our results show that adaptively utilizing indexes in a structural join improves the performance by taking advantage of both sequential scans and index probes     

Fabrication and Tunable Dielectric Properties of (Ba0.7Sr0.3)TiO3-Glass-Based Thick-Film Capacitors

Ferroelectric glass–ceramics of composition 0.90 (Ba_0.7Sr_0.3) TiO₃–0.10(B₂O₃:SiO₂) (0.90 BST:0.10 BS) synthesized by sol–gel method have been used for the preparation of dielectric thick-film inks. The particle dispersion of the glass–ceramic powders in the thick-film ink formulations have been studied through rheological measurements for fabricating thick-film capacitors by screen printing technique. The thick films derived from such glass–ceramics are found to sinter at considerably lower temperatures than the pure ceramic, and exhibit good dielectric characteristics with a tunability of 32% at 1 MHz under a dc bias field of 35 kV/cm.     

Deep Multimodal Fusion: A Hybrid Approach

We propose a novel hybrid model that exploits the strength of discriminative classifiers along with the representation power of generative models. Our focus is on detecting multimodal events in time varying sequences as well as generating missing data in any of the modalities. Discriminative classifiers have been shown to achieve higher performances than the corresponding generative likelihood-based classifiers. On the other hand, generative models learn a rich informative space which allows for data generation and joint feature representation that discriminative models lack. We propose a new model that jointly optimizes the representation space using a hybrid energy function. We employ a Restricted Boltzmann Machines (RBMs) based model to learn a shared representation across multiple modalities with time varying data. The Conditional RBMs (CRBMs) is an extension of the RBM model that takes into account short term temporal phenomena. The hybrid model involves augmenting CRBMs with a discriminative component for classification. For these purposes we propose a novel Multimodal Discriminative CRBMs (MMDCRBMs) model. First, we train the MMDCRBMs model using labeled data by training each modality, followed by training a fusion layer. Second, we exploit the generative capability of MMDCRBMs to activate the trained model so as to generate the lower-level data corresponding to the specific label that closely matches the actual input data. We evaluate our approach on ChaLearn dataset, audio-mocap, as well as the Tower Game dataset, mocap-mocap as well as three multimodal toy datasets. We report classification accuracy, generation accuracy, and localization accuracy and demonstrate its superiority compared to the state-of-the-art methods.     

Reduction in harmonic distortion through use of aperiodic timing in frequency demodulation

It is demonstrated, theoretically and experimentally, that aperiodic timing based on the FM signal, as opposed to periodic timing based on a reference carrier, can lead to a significant reduction in harmonic distortion in an FM demodulator based on an exclusive OR gate and a sample and hold structure. A 20-dB improvement in harmonic distortion can be readily achieved.     

An Online Algorithm for a Problem in Scheduling with?Set-ups?and Release Times

We address the problem of sequential single machine scheduling of jobs with release times, where jobs are classified into types, and the machine must be properly configured to handle jobs of a given type. The objective is to minimize the maximum flow time (time from release until completion) of any job. We consider this problem under the assumptions of sequence independent set-up times and item availability with the objective of minimizing the maximum flow time. We present an online algorithm that is O(1)-competitive, that is, always gets within a constant factor of optimal. We also show that exact offline optimization of maximum flow time is NP-hard.     

Flocculation of kaolinite suspensions in water by chitosan

Flocculation of kaolinite suspensions in water using chitosan was studied in the pH range 5-9 and the turbidity ranging from 10 to 160 NTU. Chitosan, in presence of trace quantities of a substance present in aqueous soil extracts, effectively reduces turbidity due to kaolinite by flocculation and settling. Flocculation efficiency is very sensitive to pH, and reaches a maximum at pH 7.5. The optimal chitosan concentration required to effect flocculation is independent of kaolinite concentration within the range examined. Flocculation is faster at higher concentrations of kaolinite and the flocs are large and settle rapidly.     

Mechanism of kaolinite and titanium dioxide flocculation using chitosan--assistance by fulvic acids?

Flocculation of kaolinite using chitosan alone as flocculant has been reported to be difficult. Detailed investigations in our laboratory have shown that kaolinite can be easily removed from aqueous suspensions by flocculation and settling using chitosan, provided the suspension medium contains traces of dissolved humic substances. The flocculation is also extremely sensitive to the pH of the suspension. Removal of suspended titanium dioxide particles in water by flocculation using chitosan in presence of humic acids is also reported for the first time. A plausible mechanism explaining these observations is proposed in this paper.     

Cholesterol esterification activities in the intestines and pancreas of the albino rat

Cholesterol esterification activities in intestines and pancreas are much greater with unsaturated fatty acids than with the saturated ones; the maximum activity is with arachidonic acid in intestines and with oleic acid in pancreas. The pancreatic cholesterol esterification activity is higher than the intestinal one.