Highly efficient cash sterilization with ultrafast and flexible Joule‐heating strategy by laser patterning

Since ancient times, humans have learned to use fire and other heating methods to fight against dangerous pathogens, like cooking raw food, sterilizing surgical tools, and disinfecting other pathogen transmission media. However, it remains difficult for current heating methods to achieve extremely fast and highly efficient sterilization simultaneously. Herein, an ultrafast and uniform heating‐based strategy with outstanding bactericidal performance is proposed. Ultra‐precise laser manufacturing is used to fabricate the Joule heater which can be rapidly heated to 90 °C in 5 s with less than 1 °C fluctuation in a large area by real‐time temperature feedback control. An over 98% bactericidal efficiency on S. aureus for 30 s and on E. coli for merely 5 s is shown. The heating strategy shows a 360 times faster acceleration compared to the commonly used steam sterilization from the suggested guidelines by the Centers for Disease Control and Prevention (CDC), indicating that high temperatures with short duration can effectively disinfect microorganisms. As a proof of concept, this heating strategy can be widely applied to sterilizing cash and various objects to help protect the public from bacteria in daily life

A Comparative of Two-Dimensional Statistical Moment Invariants Features in Formulating an Automated Probabilistic Machine Learning Identification Algorithm for Forensic Application

IBIS, ALIS, EVOFINDER, and CONDOR are the massive ballistics computerised technological machines that have typically been utilisedin forensic laboratories to automatically locate similarities between images of cartridge cases and bullets. However, it imposed a long execution time and requires physical interpretation to consolidate the analysis results when employing these market-available technologies to accomplish ballistics matching tasks. Therefore, the principalobjective of this study is to propose an improvised automated probabilistic machine learningidentification algorithm by extracting the two-dimensional (2D) statistical moment invariants from the segmented region of interest (ROI) corresponding to the cartridge case and bullets images. To pursue this principal objective, several 2D statistical moment invariants have been compared and tested to determine the most suitable feature set applied in the proposed identification algorithm. The 2D statistical moment invariants employed include Orthogonal Legendre moments (OLM), Hu moments (HM), Tsirikolias-Mertzois moments (TMM), Pan-Keane moments (PKM), and Central Geometric moments (CGM). Moreover, the proposed identification algorithm is also tested in different scenarios, including based on the classification of strength association measurements between the extracted feature sets. The empirical results in this article revealed that the proposed identification algorithm applied with the CGM comprising the weak association classification yielded the best identification accuracy rates, which are >96.5% across all the sample sizes of thetrainingset. Theseempiricalresults also conveyed that the superior proposed identification algorithm in this research could be developed as a mobile application for ballistics identification that can significantly reduce the time taken and conveniently perform the ballistics identification tasks

Effects of once-weekly exenatide on cardiovascular outcomes in type 2 diabetes

BACKGROUND: The cardiovascular effects of adding once-weekly treatment with exenatide to usual care in patients with type 2 diabetes are unknown. METHODS: We randomly assigned patients with type 2 diabetes, with or without previous cardiovascular disease, to receive subcutaneous injections of extended-release exenatide at a dose of 2 mg or matching placebo once weekly. The primary composite outcome was the first occurrence of death from cardiovascular causes, nonfatal myocardial infarction, or nonfatal stroke. The coprimary hypotheses were that exenatide, administered once weekly, would be noninferior to placebo with respect to safety and superior to placebo with respect to efficacy. RESULTS: In all, 14,752 patients (of whom 10,782 [73.1%] had previous cardiovascular disease) were followed for a median of 3.2 years (interquartile range, 2.2 to 4.4). A primary composite outcome event occurred in 839 of 7356 patients (11.4%; 3.7 events per 100 person-years) in the exenatide group and in 905 of 7396 patients (12.2%; 4.0 events per 100 person-years) in the placebo group (hazard ratio, 0.91; 95% confidence interval [CI], 0.83 to 1.00), with the intention-to-treat analysis indicating that exenatide, administered once weekly, was noninferior to placebo with respect to safety (P<0.001 for noninferiority) but was not superior to placebo with respect to efficacy (P=0.06 for superiority). The rates of death from cardiovascular causes, fatal or nonfatal myocardial infarction, fatal or nonfatal stroke, hospitalization for heart failure, and hospitalization for acute coronary syndrome, and the incidence of acute pancreatitis, pancreatic cancer, medullary thyroid carcinoma, and serious adverse events did not differ significantly between the two groups. CONCLUSIONS: Among patients with type 2 diabetes with or without previous cardiovascular disease, the incidence of major adverse cardiovascular events did not differ significantly between patients who received exenatide and those who received placebo