Knowledge representation using interval-valued fuzzy formal concept lattice

Formal concept analysis (FCA) is a mathematical framework for data analysis and processing tasks. Based on the lattice and order theory, FCA derives the conceptual hierarchies from the relational information systems. From the crisp setting, FCA has been extended to fuzzy environment. This extension is aimed at handling the uncertain and vague information represented in the form of a formal context whose entries are the degrees from the scale [0, 1]. The present study analyzes the fuzziness in a given many-valued context which is transformed into a fuzzy formal context, to provide an insight into generating the fuzzy formal concepts from the fuzzy formal context. Furthermore, considering that a major problem in FCA with fuzzy setting is to reduce the number of fuzzy formal concepts thereby simplifying the corresponding fuzzy concept lattice structure, the current paper solves the problem by linking an interval-valued fuzzy graph to the fuzzy concept lattice. For this purpose, we propose an algorithm for generating the interval-valued fuzzy formal concepts. To measure the weight of fuzzy formal concepts, an algorithm is proposed using Shannon entropy. The knowledge represented by formal concepts using interval-valued fuzzy graph is compared with entropy-based-weighted fuzzy concepts at chosen threshold.     

Tuning the crystal structure and optical properties of selective area grown InGaAs nanowires

Catalyst-free InGaAs nanowires grown by selective area epitaxy are promising building blocks for future optoelectronic devices in the infrared spectral region.D...     

Using telephone call rates and nurse-to-patient ratios as measures of resilient performance under high patient flow conditions

Patient admissions, discharges, and transfers are high work demand activities that have been associated with 30-day readmissions and increased patient mortality. Most mitigation strategies target peak demand, but variable demand may be more significant. Self-organizing holarchic open systems (SOHOs) and resilience engineering frameworks may explain system behavior, but a few quantitative studies of resilient organizational performance have been published. We used three measures to explore SOHO and resilience engineering constructs. We collected hourly data over 2 years, from five inter-related units in a cardiovascular disease division of a metropolitan teaching hospital. Our results show that information flows (inbound, outbound, answered, and unanswered telephone calls) representing anticipatory management are related to patient flows (patient admissions discharges and transfers) and nurse-staffing levels (nurse-to-patient ratios). We also found overall system stability despite high patient flow effects in lower level units. Unexpectedly, the time to recovery from high patient flow events lasted up to 7 days. We conclude that constructs proposed by resilience engineering can be quantified using simple measures collated within routine operations. The application of nonlinear statistical analyses can uncover important insights about resilient performance that may assist managers in better preparing for managing and recovering from unexpected variation in patient flow.

     

Monotone Piecewise Affine Systems

Piecewise affine (PWA) systems are autonomous systems with discontinuous vector fields which are affine ordinary differential equations at the points of continuity. These systems have applications to many fields of engineering, including systems biology and traffic engineering. We define what it means for a PWA system to be monotone, and we provide a set of sufficient conditions for monotonicity of PWA systems with hyperrectangular invariants. Such sufficient conditions are useful for understanding the dynamics of such PWA systems and for designing controllers for qualitative, reference tracking. We apply these results towards the drug-discovery problem for the cancer-related p53 pathway.     

FUZZY CLUSTERING-BASED FORMAL CONCEPT ANALYSIS FOR ASSOCIATION RULES MINING

Formal Concept Analysis (FCA), in which data is represented as a formal context, offers a framework for Association Rules Mining (ARM) by handling functional dependencies in the data. However, with the size of the formal context, the number of rules grows exponentially. In this article, we apply Fuzzy K-Means clustering on the data set to reduce the formal context and FCA on the reduced data set for mining association rules. With experiments on two real-world healthcare data sets, we offer the evidence for performance of FKM-based FCA in mining association rules.     

Detection of Spammers in Twitter marketing: A Hybrid Approach Using Social Media Analytics and Bio Inspired Computing

Customer engagement is drastically improved through Web 2.0 technologies, especially social media platforms like Twitter. These platforms are often used by organizations for marketing, of which creation of numerous spam profiles for content promotion is common. The present paper proposes a hybrid approach for identifying the spam profiles by combining social media analytics and bio inspired computing. It adopts a modified K-Means integrated Levy flight Firefly Algorithm (LFA) with chaotic maps as an extension to Firefly Algorithm (FA) for spam detection in Twitter marketing. A total of 18,44,701 tweets have been analyzed from 14,235 Twitter profiles on 13 statistically significant factors derived from social media analytics. A Fuzzy C-Means Clustering approach is further used to identify the overlapping users in two clusters of spammers and non-spammers. Six variants of K-Means integrated FA including chaotic maps and levy flights are tested. The findings indicate that FA with chaos for tuning attractiveness coefficient using Gauss Map converges to a working solution the fastest. Further, LFA with chaos for tuning the absorption coefficient using sinusoidal map outperforms the rest of the approaches in terms of accuracy.     

Review and Implementation of Topic Modeling in Hindi

ABSTRACT

Due to the widespread usage of electronic devices and the growing popularity of social media, a lot of text data is being generated at the rate never seen before. It is not possible for humans to read all data generated and find what is being discussed in his field of interest. Topic modeling is a technique to identify the topics present in a large set of text documents. In this paper, we have discussed the widely used techniques and tools for topic modeling. There has been a lot of research on topic modeling in English, but there is not much progress in the resource-scarce languages like Hindi despite Hindi being spoken by millions of people across the world. In this paper, we have discussed the challenges faced in developing topic models for Hindi. We have applied Latent Semantic Indexing (LSI), Non-negative Matrix Factorization (NMF), and Latent Dirichlet Allocation (LDA) algorithms for topic modeling in Hindi. The outcomes of the topic model algorithms are usually difficult to interpret for the common user. We have used various visualization techniques to represent the outcomes of topic modeling in a meaningful way. Then we have used the metrics like perplexity and coherence to evaluate the topic models. The results of Topic modeling in Hindi seem to be promising and comparable to some results reported in the literature on English datasets.     

Thiocyanate‐Free Ru(II) Sensitizers with a 4,4′‐Dicarboxyvinyl‐2,2′‐bipyridine Anchor for Dye‐Sensitized Solar Cells

A new class of thiocyanate‐free Ru(II) sensitizers with 4,4′‐dicarboxyvinyl‐2,2′‐bipyridine anchor and two trans‐oriented pyrid‐2‐yl pyrazolate (or triazolate) functional chromophores is synthesized, characterized, and evaluated in dye‐sensitized solar cells (DSCs). Despite their enhanced red response and absorptivity when compared to the parent sensitizer TFRS‐2 that possesses standard 4,4′‐dicarboxyl‐2,2′‐bipyridine anchor and shows the best conversion efficiency of η = 9.82%, the newly synthesized carboxyvinyl‐pyrazolate sensitizers, TFRS‐11 – TFRS‐13 , exhibit inferior performance characteristics in terms of short‐circuit current density (J_SC), open‐circuit voltage (V_OC), and power conversion efficiency (η), the latter being recorded to be in the range 5.60–7.62%. The reduction in device efficiencies is attributed to a combination of poor packing of these sensitizers on the TiO₂ surface and less positive ground‐state oxidation potentials, which, respectively, increase charge recombination with I₃^? in electrolytes and impede the regeneration of sensitizers by I^? anions. The latter obstacle can be circumvented in part by the replacement of the pyrazolates with triazolates, forming the TFRS‐14 sensitizer, which exhibits an improved J_SC, V_OC, and η of 16.4 mAcm^?2, 0.77 V, and 9.02%, respectively.     

Enzyme‐Mediated Deposition of a TiO2 Coating onto Biofunctionalized WS2 Chalcogenide Nanotubes

A chemically specific and facile method for the biofunctionalization of WS₂ nanotubes (NT‐WS₂) is reported. The covalent modification strategy is based on the affinity of the nitrilotriacetic acid (NTA) side chain, which serves as a ligand for the surface binding to NT‐WS₂ and simultaneously as an anchor group for the binding of His‐tagged proteins to the polymer backbone. The polymer functionalized WS₂ nanotubes can be solubilized either in water or organic solvents; they are stable for at least one week. The probes were characterized by FT‐IR and UV‐vis spectroscopy. The immobilization of silicatein, a hydrolytic protein encountered in marine sponges, was visualized by scanning force microscopy (SFM) and confocal laser scanning microscopy (CLSM). The formation of the biotitania coating mediated by the immobilized silicatein onto the surface was characterized by scanning electron microscopy (SEM), and transmission electron microscopy (TEM).