Research on effectiveness of technology transfer in technology alliances: Evidence from Turkish SMEs

Many SMEs homed in newly industrialised countries are successful international players despite limited technological infrastructure and R&D resources. This study bridges a gap in the extant literature by examining the relationships between characteristics of partnership relationships, knowledge sharing and the effectiveness of technology transfer in partnerships between SMEs in developing countries and firms from developed countries. By studying data from Turkish SMEs and using partial least squares structural equation modelling, we find that explicit knowledge sharing forms the basis of technology transfer. Moreover, our findings demonstrate that explicit knowledge sharing is strongly contingent upon formalised technical support while trust and technical support seemed to be important antecedents of tacit knowledge sharing

On the critical level-curvature distribution

The parametric motion of energy levels for non-interacting electrons at the Anderson localization critical point is studied by computing the energy level-curvatures for a quasiperiodic ring with twisted boundary conditions. We find a critical distribution which has the universal random matrix theory form ${\bar P}(K)\sim |K|^{-3}$ for large level-curvatures $|K|$ corresponding to quantum diffusion, although overall it is close to approximate log-normal statistics corresponding to localization. The obtained hybrid distribution resembles the critical distribution of the disordered Anderson model and makes a connection to recent experimental data.Comment: 4 pages, 3 figure

An evolving network model with community structure

Many social and biological networks consist of communities—groups of nodes within which connections are dense, but between which connections are sparser. Recently, there has been considerable interest in designing algorithms for detecting community structures in real-world complex networks. In this paper, we propose an evolving network model which exhibits community structure. The network model is based on the inner-community preferential attachment and inter-community preferential attachment mechanisms. The degree distributions of this network model are analysed based on a mean-field method. Theoretical results and numerical simulations indicate that this network model has community structure and scale-free properties

A preferential attachment model with random initial degrees

In this paper, a random graph process ${G(t)}_{t\geq 1}$ is studied and its degree sequence is analyzed. Let $(W_t)_{t\geq 1}$ be an i.i.d. sequence. The graph process is defined so that, at each integer time $t$, a new vertex, with $W_t$ edges attached to it, is added to the graph. The new edges added at time t are then preferentially connected to older vertices, i.e., conditionally on $G(t-1)$, the probability that a given edge is connected to vertex i is proportional to $d_i(t-1)+\delta$, where $d_i(t-1)$ is the degree of vertex $i$ at time $t-1$, independently of the other edges. The main result is that the asymptotical degree sequence for this process is a power law with exponent $\tau=\min\{\tau_{W}, \tau_{P}\}$, where $\tau_{W}$ is the power-law exponent of the initial degrees $(W_t)_{t\geq 1}$ and $\tau_{P}$ the exponent predicted by pure preferential attachment. This result extends previous work by Cooper and Frieze, which is surveyed.Comment: In the published form of the paper, the proof of Proposition 2.1 is incomplete. This version contains the complete proo

A network biology approach to prostate cancer

There is a need to identify genetic mediators of solid-tumor cancers, such as prostate cancer, where invasion and distant metastases determine the clinical outcome of the disease. Whole-genome expression profiling offers promise in this regard, but can be complicated by the challenge of identifying the genes affected by a condition from the hundreds to thousands of genes that exhibit changes in expression. Here, we show that reverse-engineered gene networks can be combined with expression profiles to compute the likelihood that genes and associated pathways are mediators of a disease. We apply our method to non-recurrent primary and metastatic prostate cancer data, and identify the androgen receptor gene (AR) among the top genetic mediators and the AR pathway as a highly enriched pathway for metastatic prostate cancer. These results were not obtained on the basis of expression change alone. We further demonstrate that the AR gene, in the context of the network, can be used as a marker to detect the aggressiveness of primary prostate cancers. This work shows that a network biology approach can be used advantageously to identify the genetic mediators and mediating pathways associated with a disease

Multiband optical variability of 3C 279 on diverse time-scales

We have monitored the flat spectrum radio quasar, 3C 279, in the optical B, V, R, and I passbands from 2018 February to 2018 July for 24 nights, with a total of 716 frames, to study flux, colour, and spectral variability on diverse time-scales. 3C 279 was observed using seven different telescopes: two in India, two in Argentina, two in Bulgaria, and one in Turkey to understand the nature of the source in optical regime. The source was found to be active during the whole monitoring period and displayed significant flux variations in B, V, R, and I passbands. Variability amplitudes on intraday basis varied from 5.20 to 17.9 per cent. A close inspection of variability patterns during our observation cycle reveals simultaneity among optical emissions from all passbands. During the complete monitoring period, progressive increase in the amplitude of variability with frequency was detected for our target. The amplitudes of variability in B, V, R, and I passbands have been estimated to be 177 per cent, 172 per cent, 171 per cent, and 158 per cent, respectively. Using the structure function technique, we found intraday time-scales ranging from ∼23 min to about 115 min. We also studied colour–magnitude relationship and found indications of mild bluer-when-brighter trend on shorter time-scales. Spectral indices ranged from 2.3 to 3.0 with no clear trend on long-term basis. We have also generated spectral energy distributions for 3C 279 in optical B, V, R, and I passbands for 17 nights. Finally, possible emission mechanisms causing variability in blazars are discussed briefly.Fil: Agarwal, Aditi. Indian Institute of Astrophysics; IndiaFil: Cellone, Sergio Aldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Complejo Astronómico "El Leoncito". Universidad Nacional de Córdoba. Complejo Astronómico "El Leoncito". Universidad Nacional de la Plata. Complejo Astronómico "El Leoncito". Universidad Nacional de San Juan. Complejo Astronómico "El Leoncito"; ArgentinaFil: Andruchow, Ileana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; ArgentinaFil: Mammana, Luis Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Complejo Astronómico "El Leoncito". Universidad Nacional de Córdoba. Complejo Astronómico "El Leoncito". Universidad Nacional de la Plata. Complejo Astronómico "El Leoncito". Universidad Nacional de San Juan. Complejo Astronómico "El Leoncito"; ArgentinaFil: Singh, Mridweeka. Aryabhatta Research Institute of Observational Sciences; IndiaFil: Anupama, G. C.. Indian Institute of Astrophysics; IndiaFil: Mihov, B.. Bulgarian Academy of Sciences; BulgariaFil: Raj, Ashish. Indian Institute of Astrophysics; IndiaFil: Slavcheva Mihova, L.. Bulgarian Academy of Sciences; BulgariaFil: Özdönmez, Aykut. Tübİtak National Observatory; TurquíaFil: Ege, Ergün. Istanbul University; Turquí

An approach for the identification of targets specific to bone metastasis using cancer genes interactome and gene ontology analysis

Metastasis is one of the most enigmatic aspects of cancer pathogenesis and is a major cause of cancer-associated mortality. Secondary bone cancer (SBC) is a complex disease caused by metastasis of tumor cells from their primary site and is characterized by intricate interplay of molecular interactions. Identification of targets for multifactorial diseases such as SBC, the most frequent complication of breast and prostate cancers, is a challenge. Towards achieving our aim of identification of targets specific to SBC, we constructed a 'Cancer Genes Network', a representative protein interactome of cancer genes. Using graph theoretical methods, we obtained a set of key genes that are relevant for generic mechanisms of cancers and have a role in biological essentiality. We also compiled a curated dataset of 391 SBC genes from published literature which serves as a basis of ontological correlates of secondary bone cancer. Building on these results, we implement a strategy based on generic cancer genes, SBC genes and gene ontology enrichment method, to obtain a set of targets that are specific to bone metastasis. Through this study, we present an approach for probing one of the major complications in cancers, namely, metastasis. The results on genes that play generic roles in cancer phenotype, obtained by network analysis of 'Cancer Genes Network', have broader implications in understanding the role of molecular regulators in mechanisms of cancers. Specifically, our study provides a set of potential targets that are of ontological and regulatory relevance to secondary bone cancer.Comment: 54 pages (19 pages main text; 11 Figures; 26 pages of supplementary information). Revised after critical reviews. Accepted for Publication in PLoS ON

Terahertz communications for 5G and beyond

A brief discussion about the exclusive properties and applications of terahertz technology is provided in this chapter. The frequency spectrum terahertz (THz) is also discussed. The applications of terahertz in the field of sensors and terahertz for communications are covered. State-of-the-art literature starting from the early to the latest research conducted is provided and analyzed in terms of the performance of terahertz systems. Terahertz, known as Tera waves or T-waves rather than submillimeter wave, has approximately a fraction of a wavelength less than 30 μm. T-wave is heavily used in sensing and imaging applications, and has no ionization hazards and is an excellent candidate frequency band to defeat the multipaths interference problems for pulse communications. The lower quantum energy of T-waves identifies its potential applications toward near-field imaging, telecommunications, spectroscopy, and sensing, including medical diagnoses and security screening. Identification of DNA signatures including complex real-time molecular dynamics through dielectric resonance is a good example of terahertz spectroscopy instruments nowadays. This concluding chapter will not only address the practical applications of terahertz communications, but also identify the research challenges that lie ahead in terms of terahertz antenna desig

Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli

Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone.National Institutes of Health (U.S.) (NIH grant P50-GM68762)National Institutes of Health (U.S.) (Grant U54-CA112967)United States. Dept. of Defense (Institute for Collaborative Biotechnologies