Flexible,Mechanically Stable,Porous Self-Standing Microfiber Network Membranes of Covalent Organic Frameworks: Preparation Method and Characterization

Covalent organic frameworks (COFs) show advantageous characteristics, such as an ordered pore structure and a large surface area for gas storage and separation, energy storage, catalysis, and molecular separation. However, COFs usually exist as difficult-to-process powders, and preparing continuous, robust, flexible, foldable, and rollable COF membranes is still a challenge. Herein, such COF membranes with fiber morphology for the first time prepared via a newly introduced template-assisted framework process are reported. This method uses electrospun porous polymer membranes as a sacrificial large dimension template for making self-standing COF membranes. The porous COF fiber membranes, besides having high crystallinity, also show a large surface area (1153 m² g⁻¹), good mechanical stability, excellent thermal stability, and flexibility. This study opens up the possibility of preparation of large dimension COF membranes and their derivatives in a simple way and hence shows promise in technical applications in separation, catalysis, and energy in the future.     

Performance of hashed cache data migration schemes on multicomputers

Many researchers approach the problem of programming distributed memory machines by assuming a global shared name space. Thus the user views the distributed memory of the machine as though it were shared. A major issue that arises at this point is how to manage the memory. When a processor accesses data stored on another processor's memory, data must be moved between the two processors. Once these data are retrieved from another processor's memory, several interesting issues are raised. Where should these data be stored locally? What transformations must be performed to the code to guarantee that the nonlocal accesses reference the correct memory location? What optimizations can be performed to reduce the time spent in accessing the nonlocal data? In this paper we examine various data migration mechanisms that allow an explicit and controlled mapping of data to memory. We describe, experimentally evaluate, and model a set of schemes for storing and retrieving off-processor array elements. The schemes are all based on using hash tables for efficient access of nonlocal data. The three different techniques evaluated are the basic hashed cache, partial enumeration, and full enumeration, the details of which are described in the paper. In all three schemes, nonlocal data are stored in hash tables—the difference is in the amount of memory used by the schemes and the retrieval mechanisms for nonlocal data.     

Studies of photoconductivity in β-Apo-8′ carotenal

The photoconductive properties of a carotenoid polyene,β-Apo-8′ carotenal in polycrystalline form has been studied. The growth of the photocurrent shows an overshoot in the growth-time curve before steady state value is attained. This behaviour of photocurrent is proposed to be due to higher value of recombination coefficient than trapping coefficient. From the temperature dependence study it is observed that the steady state photocurrent, at first increases with increase of temperature, attains a maximum at a particular temperatureT _max and then decreases with temperature. TheT _max value agrees with the temperature above and below which steady state photocurrent is attained differently. Monomolecular and bimolecular recombination processes at two temperature regimes are proposed to account for the observed behaviour. The dependence of photocurrent with excitation light intensity and wavelength study provide information on the carrier generation processes. The fast decay of photocurrent have been observed at different temperatures and from this study the decay constant is calculated and it is found to be temperature independent.     

Let There be Light: Polymeric Micelles with Upper Critical Solution Temperature as Light‐Triggered Heat Nanogenerators for Combating Drug‐Resistant Cancer

Complete drug release and efficient drug retention are two critical factors in reversing drug resistance in cancer therapy. In this regard, polymeric micelles with an upper critical solution temperature (UCST) are designed as a new exploration to reverse drug resistance. The amphiphilic UCST‐type block copolymers are used to encapsulate photothermal agent IR780 and doxorubicin (DOX) simultaneously. The integrated UCST‐type drug nanocarriers show light‐triggered multiple synergistic effects to reverse drug resistance and are expected to kill three birds with one stone: First, owing to the photothermal effect of IR780, the nanocarriers will be dissociated upon exposure to laser irradiation, leading to complete drug release. Second, the photothermal effect‐induced hyperthermia is expected to avoid the efflux of DOX and realize efficient drug retention. Last but not least, photothermal ablation of cancer cells can be achieved after laser irradiation. Therefore, the UCST‐type drug nanocarriers provide a new strategy in reversing drug resistance in cancer therapy.     

Unknown input modeling and robust fault diagnosis using black box observers

A key issue that needs to be addressed while performing fault diagnosis using black box models is that of robustness against abrupt changes in unknown inputs. A fundamental difficulty with the robust FDI design approaches available in the literature is that they require some a priori knowledge of the model for unmeasured disturbances or modeling uncertainty. In this work, we propose a novel approach for modeling abrupt changes in unmeasured disturbances when innovation form of state space model (i.e. black box observer) is used for fault diagnosis. A disturbance coupling matrix is developed using singular value decomposition of the extended observability matrix and further used to formulate a robust fault diagnosis scheme based on generalized likelihood ratio test. The proposed modeling approach does not require any a priori knowledge of how these faults affect the system dynamics. To isolate sensor and actuator biases from step jumps in unmeasured disturbances, a statistically rigorous method is developed for distinguishing between faults modeled using different number of parameters. Simulation studies on a heavy oil fractionator example show that the proposed FDI methodology based on identified models can be used to effectively distinguish between sensor biases, actuator biases and other soft faults caused by changes in unmeasured disturbance variables. The fault tolerant control scheme, which makes use of the proposed robust FDI methodology, gives significantly better control performance than conventional controllers when soft faults occur. The experimental evaluation of the proposed FDI methodology on a laboratory scale stirred tank temperature control set-up corroborates these conclusions.     

Fraudulent Firm Classification: A Case Study of an External Audit

This paper is a case study of visiting an external audit company to explore the usefulness of machine learning algorithms for improving the quality of an audit work. Annual data of 777 firms from 14 different sectors are collected. The Particle Swarm Optimization (PSO) algorithm is used as a feature selection method. Ten different state-of-the-art classification models are compared in terms of their accuracy, error rate, sensitivity, specificity, F measures, Mathew’s Correlation Coefficient (MCC), Type-I error, Type-II error, and Area Under the Curve (AUC) using Multi-Criteria Decision-Making methods like Simple Additive Weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results of Bayes Net and J48 demonstrate an accuracy of 93% for suspicious firm classification. With the appearance of tremendous growth of financial fraud cases, machine learning will play a big part in improving the quality of an audit field work in the future.