Multi-fingered robotic hand

A robotic hand is presented having a plurality of fingers, each having a plurality of joints pivotally connected one to the other. Actuators are connected at one end to an actuating and control mechanism mounted remotely from the hand and at the other end to the joints of the fingers for manipulating the fingers and passing externally of the robot manipulating arm in between the hand and the actuating and control mechanism. The fingers include pulleys to route the actuators within the fingers. Cable tension sensing structure mounted on a portion of the hand are disclosed, as is covering of the tip of each finger with a resilient and pliable friction enhancing surface

Cancers of the Appendix: Review of the Literatures

Cancers of the appendix are rare. Most of them are found accidentally on appendectomies performed for appendicitis. When reviewed, majority of the tumors were carcinoid, adenoma, and lymphoma. Adenocarcinomas of appendix are only 0.08% of all cancers and the treatment remains controversial. Here we are reporting a 46-year-old male presented with symptoms of appendicitis, diagnosed with adenocarcinoma of the appendix. The patient was treated with appendectomy and refused further surgical intervention to complete hemicolectomy. Up to date, he remains asymptomatic. We performed literature review of the tumors of the appendix. Most of the benign conditions are treated with surgery alone. Lymphomas require CHOP-like chemotherapy and carcinoid syndrome treatment with somatostatin analogues. It is generally recommended that right hemicolectomy is the preferred treatment for adenocarcinoma of appendix. The role of chemotherapy is unclear due to lacking randomized trials but seems to be accepted if there is lymph node involvement or peritoneal seeding

Oxidation resistance of graphene-coated Cu and Cu/Ni alloy

The ability to protect refined metals from reactive environments is vital to many industrial and academic applications. Current solutions, however, typically introduce several negative effects, including increased thickness and changes in the metal physical properties. In this paper, we demonstrate for the first time the ability of graphene films grown by chemical vapor deposition to protect the surface of the metallic growth substrates of Cu and Cu/Ni alloy from air oxidation. SEM, Raman spectroscopy, and XPS studies show that the metal surface is well protected from oxidation even after heating at 200 \degree C in air for up to 4 hours. Our work further shows that graphene provides effective resistance against hydrogen peroxide. This protection method offers significant advantages and can be used on any metal that catalyzes graphene growth

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Robotic hand-eye motor learning

This thesis investigates the use of neural networks and nonlinear estimation in robotic motor learning. It presents a detailed experimental investigation of the performance and parametric sensitivity of resource-allocating neural networks along with a new learning algorithm that offers rapid adaptation and excellent accuracy. It also includes an appendix that relates feed-forward neural networks to familiar mathematical ideas. In addition, it presents two learning hand-eye calibration systems, one based on neural networks and the other on nonlinear estimation. The network-based system learns to correct robot positioning errors arising from the use of nominal system kinematics, while the estimation-based system identifies the robot's kinematic parameters. Both systems employ the same two-link robot with stereo vision, and include noise and various other error sources. The network-based system is robust to all error sources considered, though noise naturally limits performance. The estimation-based system has significantly better performance when the robot and vision systems are well modeled, but is extremely sensitive to unmodeled error sources and noise. Finally, it presents a robot control system based on neural networks that learns to catch balls perfectly without requiring explicit programming or conventional controllers. It uses only feed-forward pursuit motions learned through practice, and is initially incapable of even moving its arm in response to external stimuli. It learns to identify and control its pursuit movements, to identify and predict ball behavior, and, with the aid of advice from a critic, to modify its movement commands to improve catching success. The system, which incorporates information from visual, arm state, and drive force sensors, characterizes control situations using input/response pairs. This allows it to learn and respond to plant variations without requiring parametric models or parameter identification. It achieves robust execution by comparing predicted and observed behavior, using inconsistencies to trigger learning and behavioral change. The architectural approach, which involves both declarative and analog knowledge as well as short- and long-term memory, can be extended to learning other sensor-motor skills like mechanical assembly and synchronizing motor actions with external processes

Copper oxide as a &quot;self-cleaning&quot; substrate for graphene growth

Commonly used techniques for cleaning copper substrates before graphene growth via chemical vapor deposition (CVD), such as rinsing with acetone, nitric, and acetic acid, and high temperature hydrogen annealing still leave residual adventitious carbon on the copper surface. This residual carbon promotes graphene nucleation and leads to higher nucleation density. We find that copper with an oxidized surface can act as a self-cleaning substrate for graphene growth by CVD. Under vacuum conditions, copper oxide thermally decomposes, releasing oxygen from the substrate surface. The released oxygen reacts with the carbon residues on the copper surface and forms volatile carbon monoxide and carbon dioxide, leaving a clean copper surface free of carbon for large-area graphene growth. Using oxidized electropolished copper foil leads to a reduction in graphene nucleation density by over a factor of 1000 when compared to using chemically cleaned oxygen free copper foil