Cable-driven robotic interface for lower limb neuromechanics identification.

This paper presents a versatile cable-driven robotic interface to investigate the single-joint joint neuromechanics of the hip, knee and ankle in the sagittal plane. This endpoint-based interface offers highly dynamic interaction and accurate position control (as is typically required for neuromechanics identification), and provides measurements of position, interaction force and EMG of leg muscles. It can be used with the subject upright, corresponding to a natural posture during walking or standing, and does not impose kinematic constraints on a joint, in contrast to existing interfaces. Mechanical evaluations demonstrated that the interface yields a rigidity above 500 N/m with low viscosity. Tests with a rigid dummy leg and linear springs show that it can identify the mechanical impedance of a limb accurately. A smooth perturbation is developed and tested with a human subject, which can be used to estimate the hip neuromechanics

The effect of internal control quality on real and accrual-based earnings management: evidence from France.

This paper examines the effect of internal control (IC) quality, measured by IC weakness disclosures, on the quality of financial statements’ information, measured by real and accrual-based earnings management. The sample consists of 686 firmyear observations of French non-financial companies listed in the SBF 120 index during the period between 2012 and 2018. Using ordinary least squares (OLS) and generalized method of moments (GMM) regression, our empirical results indicate that IC weakness disclosures are positively and significantly related to real activities manipulation and negatively associated with discretionary accruals. This provides empirical evidence that a good system of IC reduces accrual-based earnings management activities and improves the reliability of financial statements; however, it cannot control real earnings management (REM). The research findings are of practical interest not only to financial analysts, auditors, and investors—guiding them to pay attention to REM activities in case of disclosures of IC weaknesses—but also to regulators, who may consider additional disclosure requirements when reporting material IC weaknesses and designing policies that could help in reducing REM practices

The amount of keratinized mucosa may not influence peri-implant health in compliant patients: A retrospective 5-year analysis

AIM (a) To investigate the influence of the keratinized mucosa (KM) on peri-implant health or disease and (b) to identify a threshold value for the width of KM for peri-implant health. MATERIALS AND METHODS The total dataset was subsampled, that is one implant was randomly chosen per patient. In 87 patients, data were extracted at baseline (prosthesis insertion) and 5 years including the width of mid-buccal KM, bleeding on probing, probing depth, plaque index and marginal bone level (MB). Spearman correlations with Holm adjustment for multiple testing were used for potential associations. RESULTS Depending on the definition of peri-implant diseases, the prevalence of peri-implantitis ranged from 9.2% (bleeding on probing threshold: <50% or ≥50%) to 24.1% (threshold: absence or the presence). The prevalence of peri-implant mucositis was similar, irrespective of the definition (54%-55.2%). The width of KM and parameters for peri-implant diseases demonstrated negligible (Spearman correlation coefficients: -0.2 < ρ < 0.2). No threshold value was detected for the width of mid-buccal KM in relation to peri-implant health. CONCLUSION The width of KM around dental implants correlated to a negligible extent with parameters for peri-implant diseases. No threshold value for the width of KM to maintain peri-implant health could be identified

The predictive power of Bitcoin prices for the realized volatility of US stock sector returns

DATA AVAILABILITY : Data used in the study are secondary published data extracted from DataStream. However, they are available on request from the authors. The models or methodology used in the study are not registered.This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets. It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns, particularly at the sectoral level of data. We specifically assess Bitcoin prices’ ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons, based on daily data from November 22, 2017, to December, 30, 2021. The findings show that Bitcoin prices have significant predictive power for US stock volatility, with an inverse relationship between Bitcoin prices and stock sector volatility. Regardless of the stock sectors or number of forecast horizons, the model that includes Bitcoin prices consistently outperforms the benchmark historical average model. These findings are independent of the volatility measure used. Using Bitcoin prices as a predictor yields higher economic gains. These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors, which is important for practitioners and policymakers.https://jfin-swufe.springeropen.comEconomic

Advancing treatment of retinal disease through in silico trials

Abstract Treating retinal diseases to prevent sight loss is an increasingly important challenge. Thanks to the configuration of the eye, the retina can be examined relatively easily in situ. Owing to recent technological development in scanning devices, much progress has been made in understanding the structure of the retina and characterising retinal biomarkers. However, treatment options remain limited and are often of low efficiency and efficacy.&amp;#xD;&amp;#xD;In recent years, the concept of in silico clinical trials has been adopted by many pharmaceutical companies to optimise and accelerate the development of therapeutics. In silico clinical trials rely on the use of mathematical models based on the physical and biochemical mechanisms underpinning a biological system. With appropriate simplifications and assumptions, one can generate computer simulations of various treatment regimens, new therapeutic molecules, delivery strategies and so forth, rapidly and at a fraction of the cost required for the equivalent experiments. Such simulations have the potential not only to hasten the development of therapies and strategies but also to&amp;#xD;optimise the use of existing therapeutics.&amp;#xD;&amp;#xD;In this paper, we review the state-of-the-art in in silico models of the retina for mathematicians, biomedical scientists and clinicians, highlighting the challenges to developing in silico clinical trials. Throughout this paper, we highlight key findings from in silico models about the physiology of the retina in health and disease. We describe the main building blocks of in silico clinical trials and identify challenges to developing in silico clinical trials of retinal diseases.</jats:p

The role of global economic conditions in forecasting gold market volatility : evidence from a GARCH-MIDAS approach

In this study, we examine the role of global economic conditions in the predictability of gold market volatility using alternative measures. Based on the available data frequency for the relevant series, we adopt the GARCH-MIDAS approach which allows for mixed-data frequencies. We find that global economic conditions contribute significantly to gold market volatility, albeit with mixed outcomes. While the results also lend support to the safe-haven properties of the gold market, the outcome can be influenced by the choice of measure for global economic conditions. For completeness, we extend the analyses to other precious metals (palladium, platinum, rhodium and silver) and find that the global economic conditions forecast the return volatility of the gold market better than these other precious metals. Our results are robust to multiple forecast horizons and offer useful insights on the plausible investment choices in the precious metals market.The National Natural Science Foundation of China and Youth Innovation Promotion Association of Chinese Academy of Sciences.http://www.elsevier.com/locate/ribaf2022-02-03hj2021Economic

OPEC news and exchange rate forecasting using dynamic Bayesian learning

We consider whether a newspaper article count index related to the organization of the petroleum exporting countries (OPEC), which rises in response to important OPEC meetings and events connected with OPEC production levels, contains predictive power for the foreign exchange rates of G10 countries. The applied Bayesian inference methodology synthesizes a wide array of established approaches to modelling exchange rate dynamics, whereby various vector-autoregressive models are considered. Monthly data from 1996:01 to 2020:08 (given an in-sample of 1986:02 to 1995:12), shows that incorporating the OPEC news-related index into the proposed methodology leads to statistical gains in out-of-sample forecasts.http://www.elsevier.com/locate/frlhj2022Economic