Properties of a single SnO2:Zn2SnO4 – Functionalized nanowire based nanosensor

Tin oxide nanowires (SnO₂ NWs) exhibit large potential for applications in sensor and detector technology. Using a flame transport synthesis method, high-quality single crystalline SnO₂ nanowires (NWs) with Zn₂SnO₄ dots functionalized surface were synthesized on a large scale. The individual SnO₂:Zn₂SnO₄ nanowire based ultraviolet photodetector and ethanol vapors nanosensors were fabricated by contacting an individual nanowire to pre-patterned Au electrodes via a FIB/SEM system. The photodetector structure exhibited excellent photoconductive performance in terms of high response to the 375 nm ultraviolet light irradiation, ultra-fast response and recovery time at different temperatures (25–300 K). It also showed a long term stability and reliability. The n-type semiconducting behavior of the SnO₂:Zn₂SnO₄, forms an excellent material for fabricating highly sensitive and rapid responding sensors, which will enable the development of high-performance multi-functional devices.     

A Novel Polymeric Substrate with Dual-Porous Structures for High-Performance Inkjet-Printed Flexible Electronic Devices

Inkjet printing has emerged as a promising low-cost and high-performance method for manufacturing printing-based devices. However, the development of optimized substrates for inkjet printing using novel materials is limited. In this study, a novel polymeric substrate optimized for flexible electronic devices is fabricated using thin-film processing and phase inversion of polyethersulfone (PES). The PES film consists of two layers of pores; the upper layer has nano-sized pores that filter the nanoparticles in the conductive ink and allow for high-density aggregation on the substrate, while the lower layer contains micro-scale pores that quickly absorb and drain the ink solvent. The two porous structures lead to higher conductivity and high-resolution printed patterns by minimizing solvent lateral diffusion. Additionally, the PES printing substrate can undergo high-temperature curing of metal nanoparticles, enabling high-resolution pattern printing with low resistance. The PES substrate is highly transparent and flexible, allowing for the fabrication of various printed electronic patterns and the production of high-performance flexible electronic devices.     

Application of Supervised SOM Algorithms in Predicting the Hepatotoxic Potential of Drugs

The hepatotoxic potential of drugs is one of the main reasons why a number of drugs never reach the market or have to be withdrawn from the market. Therefore, the evaluation of the hepatotoxic potential of drugs is an important part of the drug development process. The aim of this work was to evaluate the relative abilities of different supervised self-organizing algorithms in classifying the hepatotoxic potential of drugs. Two modifications of standard counter-propagation training algorithms were proposed to achieve good separation of clusters on the self-organizing map. A series of optimizations were performed using genetic algorithm to select models developed with counter-propagation neural networks, X-Y fused networks, and the two newly proposed algorithms. The cluster separations achieved by the different algorithms were evaluated using a simple measure presented in this paper. Both proposed algorithms showed a better formation of clusters compared to the standard counter-propagation algorithm. The X-Y fused neural network confirmed its high ability to form well-separated clusters. Nevertheless, one of the proposed algorithms came close to its clustering results, which also resulted in a similar number of selected models.     

Highly Customizable Bone Fracture Fixation through the Marriage of Composites and Screws

Open reduction internal fixation (ORIF) metal plates provide exceptional support for unstable bone fractures; however, they often result in debilitating soft-tissue adhesions and their rigid shape cannot be easily customized by surgeons. In this work, a surgically feasible ORIF methodology, called AdhFix, is developed by combining screws with polymer/hydroxyapatite composites, which are applied and shaped in situ before being rapidly cured on demand via high-energy visible-light-induced thiol–ene coupling chemistry. The method is developed on porcine metacarpals with transverse and multifragmented fractures, resulting in strong and stable fixations with a bending rigidity of 0.28 (0.03) N m² and a maximum load before break of 220 (15) N. Evaluations on human cadaver hands with proximal phalanx fractures show that AdhFix withstands the forces from finger flexing exercises, while short- and long-term in vivo rat femur fracture models show that AdhFix successfully supports bone healing without degradation, adverse effects, or soft-tissue adhesions. This procedure represents a radical new approach to fracture fixation, which grants surgeons unparalleled customizability and does not result in soft-tissue adhesions.     

On testing transitivity in online preference learning

Machine Learning - The efficiency of state-of-the-art algorithms for the dueling bandits problem is essentially due to a clever exploitation of (stochastic) transitivity properties of pairwise...     

Multi-walled carbon nanotube-supported Ni@Pd core–shell electrocatalyst for direct formate fuel cells

Journal of Applied Electrochemistry - In the present work, Ni@Pd core–shell nanoparticles are successfully deposited on multi-walled carbon nanotubes as support and investigated their...     

On the Tightness of Semidefinite Relaxations for Rotation Estimation

Journal of Mathematical Imaging and Vision - Why is it that semidefinite relaxations have been so successful in numerous applications in computer vision and robotics for solving non-convex...     

Attention-Based and Time Series Models for Short-Term Forecasting of COVID-19 Spread

The growing number of COVID-19 cases puts pressure on healthcare services and public institutions worldwide. The pandemic has brought much uncertainty to the global economy and the situation in general. Forecasting methods and modeling techniques are important tools for governments to manage critical situations caused by pandemics, which have negative impact on public health. The main purpose of this study is to obtain short-term forecasts of disease epidemiology that could be useful for policymakers and public institutions to make necessary short-term decisions. To evaluate the effectiveness of the proposed attention-based method combining certain data mining algorithms and the classical ARIMA model for short-term forecasts, data on the spread of the COVID-19 virus in Lithuania is used, the forecasts of epidemic dynamics were examined, and the results were presented in the study. Nevertheless, the approach presented might be applied to any country and other pandemic situations. The COVID-19 outbreak started at different times in different countries, hence some countries have a longer history of the disease with more historical data than others. The paper proposes a novel approach to data registration and machine learning-based analysis using data from attention-based countries for forecast validation to predict trends of the spread of COVID-19 and assess risks.