critical relative indentation depth in carbon based thin films

Abstract The thin film hardness estimation by nanoindentation is influenced by substrate beyond a critical relative indentation depth (CRID). In this study we developed a methodology to identify the CRID in amorphous carbon film. Three types of amorphous carbon film deposited on silicon have been studied. The nanoindentation tests were carried out applying a 0.1–10 mN load range on a Berkovich diamond tip, leading to penetration depth-to-film thickness ratios of 8–100%. The work regained during unloading ( W e ) and the work performed during loading ( W t ) was estimated for each indentation. The trend of unload-to-load ratio ( W e / W t ) data as a function of depth has been studied. W e / W t depth profiles showed a sigmoid trend and the data were fitted by means of a Hill sigmoid equation. Using Hill sigmoid fit and a simple analytical method it is possible to estimate CRID of carbon based films

Economic analysis of remote monitoring in patients with implantable cardioverter defibrillators or cardiac resynchronization therapy defibrillators in the Trento area, Italy

IntroductionRemote monitoring (RM) technologies have the potential to improve patient care by increasing compliance, providing early indications of heart failure (HF), and potentially allowing for therapy optimization to prevent HF admissions. The aim of this retrospective study was to assess the clinical and economic consequences of RM vs. standard monitoring (SM) through in-office cardiology visits, in patients carrying a cardiac implantable electronic device (CIED).MethodsClinical and resource consumption data were extracted from the Electrophysiology Registry of the Trento Cardiology Unit, which has been systemically collecting patient information from January 2011 to February 2022. From a clinical standpoint, survival analysis was conducted, and incidence of cardiovascular (CV) related hospitalizations was measured. From an economic standpoint, direct costs of RM and SM were collected to compare the cost per treated patient over a 2-year time horizon. Propensity score matching (PSM) was used to reduce the effect of confounding biases and the unbalance of patient characteristics at baseline.ResultsIn the enrollment period, N = 402 CIED patients met the inclusion criteria and were included in the analysis (N = 189 patients followed through SM; N = 213 patients followed through RM). After PSM, comparison was limited to N = 191 patients in each arm. After 2-years follow-up since CIED implantation, mortality rate for any cause was 1.6% in the RM group and 19.9% in the SM group (log-rank test, p < 0.0001). Also, a lower proportion of patients in the RM group (25.1%) were hospitalized for CV-related reasons, compared to the SM group (51.3%; p < 0.0001, two-sample test for proportions). Overall, the implementation of the RM program in the Trento territory was cost-saving in both payer and hospital perspectives. The investment required to fund RM (a fee for service in the payer perspective, and staffing costs for hospitals), was more than offset by the lower rate of hospitalizations for CV-related disease. RM adoption generated savings of −€4,771 and −€6,752 per patient in 2 years, in the payer and hospital perspective, respectively.ConclusionRM of patients carrying CIED improves short-term (2-years) morbidity and mortality risks, compared to SM and reduces direct management costs for both hospitals and healthcare services

AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb : a review

Over the past few years, there has been a noticeable surge in efforts to design novel tools and approaches that incorporate Artificial Intelligence (AI) into rehabilitation of persons with lower-limb impairments, using robotic exoskeletons. The potential benefits include the ability to implement personalized rehabilitation therapies by leveraging AI for robot control and data analysis, facilitating personalized feedback and guidance. Despite this, there is a current lack of literature review specifically focusing on AI applications in lower-limb rehabilitative robotics. To address this gap, our work aims at performing a review of 37 peer-reviewed papers. This review categorizes selected papers based on robotic application scenarios or AI methodologies. Additionally, it uniquely contributes by providing a detailed summary of input features, AI model performance, enrolled populations, exoskeletal systems used in the validation process, and specific tasks for each paper. The innovative aspect lies in offering a clear understanding of the suitability of different algorithms for specific tasks, intending to guide future developments and support informed decision-making in the realm of lower-limb exoskeleton and AI applications