The Impact of Capital Structure on Firm’s Performance ( A case of Non-Financial Sector of Pakistan)

This paper tends to investigate the impact of capital structure on the firm performance of the firms from the non-financial sector of Pakistan. Non-financial firms listed on Karachi Stock Exchange are taken as the sample size for the study. For measuring the performance of the firms Return on Assets (ROA), Return on Equity (ROE), Net Profit Margin (NPM) and Earning per Share (EPS) are used as proxies. Short Term Debt (STD), Long Term Debt (LTD) and Leverage of the Firm or Total Debt (LEV) are variables for the capital structure. Controlled variables installed in the study are Size of the Firms (SIZE), Sales Growth (SALG), Assets Growth (ASSG) and Assets Turnover or Efficiency of the Firm (ASST). The total firms were 441, due to incomplete data it came down to 380 firms. Ordinary Least Square (OLS) method is used to analyze the performance, data is taken from 2005 to 2011 i.e. 7 years. Short Term Debt (STD), Long Term Debt (LTD) and Leverage of the Firm (LEV) have a negatively affected Return on Assets (ROA). Return on Equity (ROE) has a negative relation with all the capital structure variables but with Long Term Debt (LTD) and Leverage of the Firm (LEV) it was insignificant. In case of Net Profit Margin (NPM) the impact was positive but was insignificant for all the variables i.e. Long Term Debt (LTD), Short Term Debt (STD) and Leverage of the Firm (LEV). All the capital structure variables negatively affected Earning per Share (EPS) and were significant. Assets Turnover affected the performance positively for all proxies except Net Profit Margin (NPM) for which it was positive but insignificant. Size of the firm positively affected the performance overall while Sales Growth (SALG) has a significantly negative impact on Return on Assets. Assets Growth was found to have on impact on the performance of the firms. Keywords: Capital Structure, Firm’s Performanc

Power, performance and reliability optimisation of on-chip interconnect by adroit use of dark silicon

Continuous transistor scaling has enabled computer architecture to integrate increasing numbers of cores on a chip. Packet switched Network-on-Chip (NoC) is envisioned as a scalable and cost effective communication fabric for multicore architectures with tens and hundreds of cores. Extreme transistor scaling (45nm and beyond) has its own share of technical challenges. For recent technology nodes, the power per transistor is not reducing at the same rate as area. Failed Dennard's Scaling has resulted in a situation where we have abundant transistors, but not enough power to switch on these transistors at the same time, a phenomenon termed Dark Silicon. Previous research on dark silicon concentrated on integrating application specific accelerators or cores to improve energy efficiency and reliability, completely neglecting the interplay of dark silicon and NoC architecture.For the first time, this thesis proposes various NoC architectures that exploit dark silicon to improve the energy efficiency, performance and reliability of the on-chip interconnect. The first proposal is an on-chip interconnect, named darkNoC, that consists of multiple NoCs where each NoC is optimised at design time using multi-vt optimisation for different voltage-frequency (VF) levels. This architecture can provide up to 52% saving in NoC energy delay product (EDP) for certain benchmarks, whereas state-of-the-art DVFS scheme only saved 15% EDP. Then, the Malleable NoC architecture is proposed, which further improves the energy efficiency of darkNoC by a combination of multiple VF optimised routers and per node VF selection, and by exploiting the heterogeneity of application workload and application-to-core mapping. Next, this thesis proposes SuperNet NoC architecture, that exchanges dark silicon for optimising the energy, performance and reliability of on-chip interconnect. SuperNet consists of two parallel NoC planes that are optimised for different VF levels, and can be configured at runtime to operate in energy efficient mode, performance mode or reliability mode. Finally, a design flow for designing custom on-chip communication for application specific MPSoCs targeting streaming applications is proposed. To reduce the runtime of the framework, a heuristic with linear time complexity is introduced for exploring exponential design space, reducing framework runtime by 27x compared to a state-of-the-art heuristic

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)