Efficiency analysis of information technology and online social networks management : an integrated DEA-Model assessment

This paper analyses the relationship between productive efficiency and online-social-networks (OSN) in Spanish telecommunications firms. A data-envelopment-analysis (DEA) is used and several indicators of business ?social Media? activities are incorporated. A super-efficiency analysis and bootstrapping techniques are performed to increase the model?s robustness and accuracy. Then, a logistic regression model is applied to characterise factors and drivers of good performance in OSN. Results reveal the company?s ability to absorb and utilise OSNs as a key factor in improving the productive efficiency. This paper presents a model for assessing the strategic performance of the presence and activity in OSN.

Un modelo no paramétrico de evaluación de la eficiencia y la gestión de las redes sociales virtuales : una aplicación a las empresas del sector de las telecomunicaciones en España

Este artículo analiza la relación entre la eficiencia productiva y las Redes Sociales Virtuales (RSV) en las empresas de telecomunicaciones en España. En una primera etapa, se aplica el análisis envolvente de datos (DEA) incorporando varios indicadores de actividad ?Social Media?. En una segunda etapa, se utiliza una regresión logística para caracterizar las empresas eficientes. Los resultados muestran que la capacidad de absorción y utilización de las RSV es un factor determinante en la mejora de la eficiencia productiva. La utilización combinada y las distintas capacidades de gestión de las RSV permiten identificarlas como un recurso heterogéneo. Este trabajo presenta un modelo para la evaluación del desempeño estratégico al abordar su presencia y actividad en RSV. ABSTRACT. This paper analyzes the relationship between the productive efficiency and the Online Social Networks - OSN in the Spanish telecommunications firms. First, a data envelopment analysis (DEA) is used and several indicators of business "Social Media" activities are incorporated. In a second stage, a logistic regression model regression is applied to characteri ze the efficient enterprises. Results show that the company's ability to absorb and utilize this OSN is a key factor in improving the productive efficiency. These results on the combined use and different management capabilities of OSN point to a definitio n of OSN as a heterogeneous resource. This paper presents a model for assessing the strategic performance to address their presence and activity in OSN.

Mining the online social network data: Influence, summarization, and organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

Single-Cell Transcriptome Analysis of Olfactory Sensory Neurons

Olfactory sensory neurons (OSNs), which detect a myriad of odorants, are known to express one allele of one olfactory receptor (OR) gene (Olfr) from the largest gene family in the mammalian genome. The OSNs expressing the same OR project their axons to the main olfactory bulb where they converge to form glomeruli. This “One neuron-one receptor rule” makes the olfactory epithelium (OE), which consists of a vast number of OSNs expressing unique ORs, one of the most heterogeneous cell populations. However, the mechanism of how the single OR allele is chosen remains unclear along with the question of whether one OSN only expresses a single OR gene, a hypothesis that has not been rigorously verified while we performed the experiments. Moreover, failure of axonal targeting to single glomerulus was observed in MeCP2 deficient OSNs where delayed development was proposed as an explanation for the phenotype. How Mecp2 mutation caused this aberrant targeting is not entirely understood.

In this dissertation, we explored the transcriptomes of single and mature OSNs by single-cell RNA-Seq to reveal their heterogeneity and further studied the OR gene expression from these isolated OSNs. The singularity of sequenced OSNs was ensured by the observation of monoallelic expression of X-linked genes from the hybrid samples from crosses between mice of different strains where strain-specific polymorphisms could be used to track the allelic origins of SNP-containing reads. The clustering of expression profiles from triplicates that originated from the same cell assured that the transcriptomic identities of OSNs were maintained through the experimental process. The average gene expression profiles of sequenced OSNs correlated well to the conventional transcriptome data of FACS-sorted Omp-positive cells, and the top-ranked expression of OR was conceded in the single-OSN transcriptomes. While exploring cellular diversity, in addition to OR genes, we revealed nearly 200 differentially expressed genes among the sequenced OSNs in this study. Among the 36 sequenced OSNs, eight cells (22.2%) showed multiple OR gene expression and the presences of additional ORs were not restricted to the neighbor loci that shared the transcriptional effect of the primary OR expression, suggesting that the “One neuron-one receptor rule” might not be strictly true at the transcription level. All of the inferable ORs, including additional co-expressed ORs, were shown to be monoallelic. Our sequencing of 21 Mecp2308 mutant OSNs, of which 62% expressed more than one OR genes, and the expression levels of the additional ORs were significantly higher than those in the wild-type, suggested that MeCP2 plays a role in the regulation of singular OR gene expression. Dual label in situ hybridization along with the sequence data revealed that dorsal and ventral ORs were co-expressed in the same Mecp2 mutant OSN, further implying that MeCP2 might be involved in regulation of OR territories in the OE. Our results suggested a new role of MeCP2 in OR gene choice and ratified that this multiple-OR expression caused by Mecp2 mutation did not accompany delayed OSN development that has been observed in the previous studies on the Mecp2 mutants.

Mining the Online Social Network Data: Influence, Summarization, and Organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

On the microstructure, growth pattern and original porosity of belemnite rostra: insights from calcitic Jurassic belemnites

Calcitic belemnite rostra are usually employed to perform paleoenvironmental studies based on geochemical data. However, several questions, such as their original porosity and microstructure, remain open, despite they are essential to make accurate interpretations based on geochemical analyses.This paper revisits and enlightens some of these questions. Petrographic data demonstrate that calcite crystals of the rostrum solidum of belemnites grow from spherulites that successively develop along the apical line, resulting in a “regular spherulithic prismatic” microstructure. Radially arranged calcite crystals emerge and diverge from the spherulites: towards the apex, crystals grow until a new spherulite is formed; towards the external walls of the rostrum, the crystals become progressively bigger and prismatic. Adjacent crystals slightly vary in their c-axis orientation, resulting in undulose extinction. Concentric growth layering develops at different scales and is superimposed and traversed by a radial pattern, which results in the micro-fibrous texture that is observed in the calcite crystals in the rostra.Petrographic data demonstrate that single calcite crystals in the rostra have a composite nature, which strongly suggests that the belemnite rostra were originally porous. Single crystals consistently comprise two distinct zones or sectors in optical continuity: 1) the inner zone is fluorescent, has relatively low optical relief under transmitted light (TL) microscopy, a dark-grey color under backscatter electron microscopy (BSEM), a commonly triangular shape, a “patchy” appearance and relatively high Mg and Na contents; 2) the outer sector is non-fluorescent, has relatively high optical relief under TL, a light-grey color under BSEM and low Mg and Na contents. The inner and fluorescent sectors are interpreted to have formed first as a product of biologically controlled mineralization during belemnite skeletal growth and the non-fluorescent outer sectors as overgrowths of the former, filling the intra- and inter-crystalline porosity. This question has important implications for making paleoenvironmental and/or paleoclimatic interpretations based on geochemical analyses of belemnite rostra.Finally, the petrographic features of composite calcite crystals in the rostra also suggest the non-classical crystallization of belemnite rostra, as previously suggested by other authors.