Tripartite analysis across business cycles in Turkey: A multi-timescale inquiry of efficiency, volatility and integration

AbstractIn the current era of globalization, deregulation and liberalization of markets have led to financial integration amongst developing and developed countries. The sudden massive inflow of capital into developing country's stock markets begs the question of whether or not the markets are sufficiently efficient to handle the increasing integration of markets. Furthermore, the relationship between the integration and efficiency of stock markets tends to be of greater importance during economic downturns. Taking Turkey as a case study owing to its economic growth and importance in two successful blocs, i.e. the EU and the OIC, we attempt to analyse the linkages between stock market efficiency and integration during the different phases of the economy. The findings of our study provide an interesting insight into the relative improvement in volatility, efficiency and integration across business cycles, in a multi time scale analysis

Interactive effect of STAT6 and IL13 gene polymorphisms on eczema status: results from a longitudinal and a cross-sectional study

BACKGROUND: Eczema is a prevalent skin disease that is mainly characterized by systemic deviation of immune response and defective epidermal barrier. Th2 cytokines, such as IL-13, and transcription factor STAT6 are key elements in the inflammatory response that characterize allergic disorders, including eczema. Previous genetic association studies showed inconsistent results for the association of single nucleotide polymorphisms (SNPs) with eczema. Our aim was to investigate whether SNPs in IL13 and STAT6 genes, which share a biological pathway, have an interactive effect on eczema risk.METHODS: Data from two independent population-based studies were analyzed, namely the Isle of Wight birth cohort study (IOW; n = 1,456) and for the purpose of replication the Swansea PAPA (Poblogaeth Asthma Prifysgol Abertawe; n = 1,445) cross-sectional study. Log-binomial regressions were applied to (i) account for the interaction between IL13 (rs20541) and STAT6 (rs1059513) polymorphisms and (ii) estimate the combined effect, in terms of risk ratios (RRs), of both risk factors on the risk of eczema.RESULTS: Under a dominant genetic model, the interaction term [IL13 (rs20541) x STAT6 (rs1059513)] was statistically significant in both studies (IOW: adjusted Pinteraction = 0.046; PAPA: Pinteraction = 0.037). The assessment of the combined effect associated with having risk genotypes in both SNPs yielded a 1.52-fold increased risk of eczema in the IOW study (95% confidence interval (CI): 1.05 -- 2.20; P = 0.028) and a 2.01-fold higher risk of eczema (95% CI: 1.29 -- 3.12; P = 0.002) in the PAPA study population.CONCLUSIONS: Our study adds to the current knowledge of genetic susceptibility by demonstrating for the first time an interactive effect between SNPs in IL13 (rs20541) and STAT6 (rs1059513) on the occurrence of eczema in two independent samples. Findings of this report further support the emerging evidence that points toward the existence of genetic effects that occur via complex networks involving gene-gene interactions (epistasis)

Dynamic Clustering and Data Aggregation for the Internet-of-Underwater-Things Networks

Advances in semiconductor technology have made it possible to have high processing powers in cheap microcontrollers, which is spawning off a revolution in the range of applications of the Internet-of-Things (IoT) and its underwater counterpart, the Internet-of-Underwater-Things (IoUT). As a result, it has now become possible and cost effective to implement powerful data processing algorithms on very cheap microcontrollers and achieve network intelligence on edge devices. In this paper, we evaluate the impact of implementing an unsupervised machine learning technique based on the k-means algorithm, as well as data aggregation, on the performance of a wireless underwater sensor network. A clustering algorithm based on the k-means algorithm is used to divide the network into clusters and to select cluster heads based on network topology and residual energy. Each cluster head collects and aggregates data from nodes within its cluster's coverage and forwards the data to the sink. The network is deployed in a shallow seabed, and it is assumed that the nodes can reach the sink using their full transmission powers. Hence, the performance evaluation compares the sum-throughput, energy efficiency and coverage probability for direct transmissions to the sink against transmissions using the cluster heads. We also propose a special consideration for disaster early warning data, which packets are assigned priority delivery and handled with minimum delay. The evaluation is performed through computer simulations and the results show over a 100% improvement in throughput for clusterbased transmissions compared to direct transmissions

Energy-aware caching and collaboration for green communication systems.

Social networks and mobile applications tend to enhance the need for high-quality content access. To meet the growing demand for data services in 5G cellular networks, it is important to develop effective content caching and distribution techniques, to reduce redundant data transmission and thereby improve network efficiency significantly. It is anticipated that energy harvesting and self-powered Small Base Stations' (SBS) are the rudimentary constituents of next-generation cellular networks. However, uncertainties in harvested energy are the primary reasons to opt for energy-efficient (EE) power control schemes to reduce SBS energy consumption and ensure the quality of services for users. Using edge collaborative caching, such EE design can also be achievable via the use of the content cache, decreasing the usage of capacity limited SBSs backhaul and reducing energy utilisation. Renewable energy (RE) harvesting technologies can be leveraged to manage the huge power demands of cellular networks. To reduce carbon footprint and improve energy efficiency, we tailored a more practical approach and propose green caching mechanisms for content distribution that utilise the content caching and renewable energy concept. Simulation results and analysis provide key insights that the proposed caching scheme brings a substantial improvement regarding content availability, cache storage capacity at the edge of cellular networks, enhances energy efficiency, and increases cache collaboration time up to 24%. Furthermore, self-powered base stations and energy harvesting are an ultimate part of next-generation wireless networks, particularly in terms of optimum economic sustainability and green energy in developing countries for the evolution of mobile networks

Bullying of medical students in Pakistan: a cross-sectional questionnaire survey.

Background: Several studies from other countries have shown that bullying, harassment, abuse or belittlement are a regular phenomenon faced not only by medical students, but also junior doctors, doctors undertaking research and other healthcare professionals. While research has been carried out on bullying experienced by psychiatrists and psychiatry trainees in Pakistan no such research has been conducted on medical students in this country. Methodology/Principal Findings: We conducted a cross-sectional questionnaire survey on final year medical students in six medical colleges of Pakistan. The response rate was 63%. Fifty-two percent of respondents reported that they had faced bullying or harassment during their medical education, about 28% of them experiencing it once a month or even more frequently. The overwhelming form of bullying had been verbal abuse (57%), while consultants were the most frequent (46%) perpetrators. Students who were slightly older, males, those who reported that their medical college did not have a policy on bullying or harassment, and those who felt that adequate support was not in place at their medical college for bullied individuals, were significantly more likely to have experienced bullying. Conclusion: Bullying or harassment is faced by quite a large proportion of medical students in Pakistan. The most frequent perpetrators of this bullying are consultants. Adoption of a policy against bullying and harassment by medical colleges, and providing avenues of support for students who have been bullied may help reduce this phenomenon, as the presence of these two was associated with decreased likelihood of students reporting having being bullied

Prediction models for childhood asthma: a systematic review

Background The inability to objectively diagnose childhood asthma before age five often results in both under‐treatment and over‐treatment of asthma in preschool children. Prediction tools for estimating a child's risk of developing asthma by school‐age could assist physicians in early asthma care for preschool children. This review aimed to systematically identify and critically appraise studies which either developed novel or updated existing prediction models for predicting school‐age asthma. Methods Three databases (MEDLINE, Embase and Web of Science Core Collection) were searched up to July 2019 to identify studies utilizing information from children ≤5 years of age to predict asthma in school‐age children (6‐13 years). Validation studies were evaluated as a secondary objective. Results Twenty‐four studies describing the development of 26 predictive models published between 2000 and 2019 were identified. Models were either regression‐based (n = 21) or utilized machine learning approaches (n = 5). Nine studies conducted validations of six regression‐based models. Fifteen (out of 21) models required additional clinical tests. Overall model performance, assessed by area under the receiver operating curve (AUC), ranged between 0.66 and 0.87. Models demonstrated moderate ability to either rule in or rule out asthma development, but not both. Where external validation was performed, models demonstrated modest generalizability (AUC range: 0.62‐0.83). Conclusion Existing prediction models demonstrated moderate predictive performance, often with modest generalizability when independently validated. Limitations of traditional methods have shown to impair predictive accuracy and resolution. Exploration of novel methods such as machine learning approaches may address these limitations for future school‐age asthma predictio

Spirometric phenotypes from early childhood to young adulthood : a Chronic Airway Disease Early Stratification study

Acknowledgements Cohort-specific acknowledgements are presented in the supplementary material. We also acknowledge collaboration with the EXPANSE consortium (funded by the EU H2020 programme, grant number 874627). We thank Elise Heuvelin, European Respiratory Society, Lausanne, Switzerland, for her assistance on the current project.Peer reviewedPublisher PD