The effect of supply chain power on bank financing

Using comprehensive bank-loan contract information, we show that the power of a firm relative to its suppliers eases its terms of bank financing, specifically through lower loan prices and less restrictive non-price contract terms. Our results are robust to controlling for product-market competition. Supply chain power enables the firm to achieve a greater level of control over its inventory, constituting a significant portion of the reduction in its overall loan cost. We argue that it is important to consider supply-chain related issues when analyzing the external-financing capacity of firms

Unsteady Natural Convection in an Initially Stratified Air-Filled Trapezoidal Enclosure Heated from Below

Natural convection is intensively explored, especially in a valley-shaped trapezoidal enclosure, because of its broad presence in both technical settings and nature. This study deals with a trapezoidal cavity, which is initially filled with linearly stratified air. Although the sidewalls remain adiabatic, the bottom wall is heated, and the top wall is cooled. For the stratified fluid (air), the temperature of the fluid adjacent to the top and the bottom walls is the same as that of the walls. Natural convection in the trapezoidal cavity is simulated in two dimensions using numerical simulations, by varying Rayleigh numbers (Ra) from 100 to 108 with constant Prandtl number, Pr = 0.71, and aspect ratio, A = 0.5. The numerical results demonstrate that the development of natural convection from the beginning is dependent on the Rayleigh numbers. According to numerical results, the development of transient flow within the enclosure owing to the predefined conditions for the boundary may be categorized into three distinct stages: early, transitional, and steady or unsteady. The flow characteristics at each of the three phases and the impact of the Rayleigh number on the flow’s growth are quantified. Unsteady natural convection flows in the enclosure are described and validated by numerical results. In addition, heat transfer through the bottom and the top surfaces is described in this study.</jats:p

Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning

Genomic data analysis is a fundamental system for monitoring pathogen evolution and the outbreak of infectious diseases. Based on bioinformatics and deep learning, this study was designed to identify the genomic variability of SARS-CoV-2 worldwide and predict the impending mutation rate. Analysis of 259044 SARS-CoV-2 isolates identified 3334545 mutations with an average of 14.01 mutations per isolate. Globally, single nucleotide polymorphism (SNP) is the most prevalent mutational event. The prevalence of C > T (52.67%) was noticed as a major alteration across the world followed by the G > T (14.59%) and A > G (11.13%). Strains from India showed the highest number of mutations (48) followed by Scotland, USA, Netherlands, Norway, and France having up to 36 mutations. D416G, F106F, P314L, UTR:C241T, L93L, A222V, A199A, V30L, and A220V mutations were found as the most frequent mutations. D1118H, S194L, R262H, M809L, P314L, A8D, S220G, A890D, G1433C, T1456I, R233C, F263S, L111K, A54T, A74V, L183A, A316T, V212F, L46C, V48G, Q57H, W131R, G172V, Q185H, and Y206S missense mutations were found to largely decrease the structural stability of the corresponding proteins. Conversely, D3L, L5F, and S97I were found to largely increase the structural stability of the corresponding proteins. Multi-nucleotide mutations GGG > AAC, CC > TT, TG > CA, and AT > TA have come up in our analysis which are in the top 20 mutational cohort. Future mutation rate analysis predicts a 17%, 7%, and 3% increment of C > T, A > G, and A > T, respectively in the future. Conversely, 7%, 7%, and 6% decrement is estimated for T > C, G > A, and G > T mutations, respectively. T > G\A, C > G\A, and A > T\C are not anticipated in the future. Since SARS-CoV-2 is mutating continuously, our findings will facilitate the tracking of mutations and help to map the progression of the COVID-19 intensity worldwide

Reactions of Ru3(CO)10(μ-dppm) with Ph3GeH: Ge–H and Ge–C bond cleavage in Ph3GeH at triruthenium clusters

The activation of Ph3GeH at the dppm-bridged cluster Ru3(CO)10(μ-dppm) [dppm = bis(diphenylphosphino)methane] has been investigated. Ru3(CO)10(μ-dppm) reacts with Ph3GeH at room temperature in the presence of Me3NO to give the new cluster products Ru3(CO)9(GePh3)(μ-dppm)(μ-H) (1) and Ru3(CO)8(GePh3)2(μ-dppm)(μ-H)2 (2) via successive oxidation-addition of two Ge–H bonds. Refluxing 1 in THF furnishes the diruthenium complex Ru2(CO)6(μ-GePh2)(μ-dppm) (3) as the major product (44%), in addition to Ru3(CO)7(μ-CO)(GePh3){μ3-PhPCH2P(Ph)C6H4}(μ-H) (4) and the known cluster Ru3(CO)9(μ-H)(μ3-Ph2PCH2PPh) (5) in 7 and 8% yields, respectively. Heating samples of cluster 2 also afforded 3 as the major product together with a small amount of Ru3(CO)7(GePh3)(μ-OH)(μ-dppm)(μ-H)2 (6). DFT calculations establish the stability of the different possible isomers for clusters 1, 2, and 6, in addition to providing insight into the mechanism for hydride fluxionality in 2. All new compounds have been characterized by analytical and spectroscopic methods, and the molecular structures of 1, 3, and 6 have been established by single-crystal X-ray diffraction analyses

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.</jats:p

A Look Back at an Ongoing Problem: Shigella dysenteriae Type 1 Epidemics in Refugee Settings in Central Africa (1993–1995)

BACKGROUND: Shigella dysenteriae type 1 (Sd1) is a cause of major dysentery outbreaks, particularly among children and displaced populations in tropical countries. Although outbreaks continue, the characteristics of such outbreaks have rarely been documented. Here, we describe the Sd1 outbreaks occurring between 1993 and 1995 in 11 refugee settlements in Rwanda, Tanzania and Democratic Republic of the Congo (DRC). We also explored the links between the different types of the camps and the magnitude of the outbreaks. METHODOLOGY/PRINCIPAL FINDINGS: Number of cases of bloody diarrhea and deaths were collected on a weekly basis in 11 refugee camps, and analyzed retrospectively. Between November 1993 and February 1995, 181,921 cases of bloody diarrhea were reported. Attack rates ranged from 6.3% to 39.1% and case fatality ratios (CFRs) from 1.5% to 9.0% (available for 5 camps). The CFRs were higher in children under age 5. In Tanzania where the response was rapidly deployed, the mean attack rate was lower than in camps in the region of Goma without an immediate response (13.3% versus 32.1% respectively). CONCLUSIONS/SIGNIFICANCE: This description, and the areas where data is missing, highlight both the importance of collecting data in future epidemics, difficulties in documenting outbreaks occurring in complex emergencies and most importantly, the need to assure that minimal requirements are met

Trapped in the prison of the mind: notions of climate-induced (im)mobility decision-making and wellbeing from an urban informal settlement in Bangladesh

The concept of Trapped Populations has until date mainly referred to people ‘trapped’ in environmentally high-risk rural areas due to economic constraints. This article attempts to widen our understanding of the concept by investigating climate-induced socio-psychological immobility and its link to Internally Displaced People’s (IDPs) wellbeing in a slum of Dhaka. People migrated here due to environmental changes back on Bhola Island and named the settlement Bhola Slum after their home. In this way, many found themselves ‘immobile’ after having been mobile—unable to move back home, and unable to move to other parts of Dhaka, Bangladesh, or beyond. The analysis incorporates the emotional and psychosocial aspects of the diverse immobility states. Mind and emotion are vital to better understand people’s (im)mobility decision-making and wellbeing status. The study applies an innovative and interdisciplinary methodological approach combining Q-methodology and discourse analysis (DA). This mixed-method illustrates a replicable approach to capture the complex state of climate-induced (im)mobility and its interlinkages to people’s wellbeing. People reported facing non-economic losses due to the move, such as identity, honour, sense of belonging and mental health. These psychosocial processes helped explain why some people ended up ‘trapped’ or immobile. The psychosocial constraints paralysed them mentally, as well as geographically. More empirical evidence on how climate change influences people’s wellbeing and mental health will be important to provide us with insights in how to best support vulnerable people having faced climatic impacts, and build more sustainable climate policy frameworks

Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012

Measurement of the Forward-Backward Asymmetry in the B -> K(*) mu+ mu- Decay and First Observation of the Bs -> phi mu+ mu- Decay

We reconstruct the rare decays $B^+ \to K^+\mu^+\mu^-$, $B^0 \to K^{*}(892)^0\mu^+\mu^-$, and $B^0_s \to \phi(1020)\mu^+\mu^-$ in a data sample corresponding to $4.4 {\rm fb^{-1}}$ collected in $p\bar{p}$ collisions at $\sqrt{s}=1.96 {\rm TeV}$ by the CDF II detector at the Fermilab Tevatron Collider. Using $121 \pm 16$ $B^+ \to K^+\mu^+\mu^-$ and $101 \pm 12$ $B^0 \to K^{*0}\mu^+\mu^-$ decays we report the branching ratios. In addition, we report the measurement of the differential branching ratio and the muon forward-backward asymmetry in the $B^+$ and $B^0$ decay modes, and the $K^{*0}$ longitudinal polarization in the $B^0$ decay mode with respect to the squared dimuon mass. These are consistent with the theoretical prediction from the standard model, and most recent determinations from other experiments and of comparable accuracy. We also report the first observation of the $B^0_s \to \phi\mu^+\mu^- decay and measure its branching ratio${\mathcal{B}}(B^0_s \to \phi\mu^+\mu^-) = [1.44 \pm 0.33 \pm 0.46] \times 10^{-6}$using$27 \pm 6$signal events. This is currently the most rare$B^0_s$ decay observed.Comment: 7 pages, 2 figures, 3 tables. Submitted to Phys. Rev. Let