Smartphone-based bulky waste classification using convolutional neural networks

Multimedia Tools and Applications - The rapid urbanization process is escalating the urban waste problem, and ineffective management has worsened the issue, leading to severe consequences to the...     

Effect of Initial Grain Size on Friction Stir Weldability for Rolled and Cast CoCrFeMnNi High-Entropy Alloys

Metals and Materials International - This study investigated the influence of the initial grain size on the plastic deformation and tunnel defects that occurred from friction stir welding of...     

A Two-Dimensional Numerical Analysis for Thermal Performance of an Intermittently Operated Radiant Floor Heating System in a Transient External Climatic Condition

Abstract

Building thermal inertia and operation control strategies have impacted on the thermal performance of a radiant floor heating system. This study conducts a two-dimensional numerical analysis of an intermittently operated radiant floor heating system using the Re-Normalization Group model with Discrete Ordinates Radiation model. A detailed numerical simulation setups and various analyses are provided, including grid independency analysis, initial condition, time step sizes and external boundary conditions. Three different weekend day intermittent operation strategies are investigated. The results showed that Case 3 designed with pre-heating of 20?h has better performance compared to Case 1 designed with pre-heating of 8?h and Case 2 designed with pre-heating of 14?h. The average indoor air temperature differences of approximate 2.1, 1.6 and 1.2 K are observed for Case 1, Case 2 and Case 3, respectively, when comparing two-time slot at 8:00am on Friday morning and Monday morning. This significantly highlights the effect of thermal inertia and the potential of energy saving due to the utilization of intermittent operation. Therefore, the current study presents numerical simulation potential in evaluating the radiant floor heating effects on indoor thermal environment, taking into account building thermal inertia and transient external climatic conditions.     

Replacing Mg2+ by Fe2+ for RNA-Cleaving DNAzymes

It has been proposed that Mg²⁺ and Fe²⁺ are very similar in interacting with ribozymes and some protein-based enzymes, but their activities with DNAzymes have yet to be studied. Here, the activity of Fe²⁺ as cofactor for a few RNA-cleaving DNAzymes is investigated. 17E is a well-studied DNAzyme that is active in the presence of many different divalent metal ions; it is highly active with Fe²⁺ with an apparent K_d of 29.7±2.3 μm and a k_obs of 1.12±0.11 min⁻¹ in the presence of 1 mm Fe²⁺ at pH 7.5. Fe²⁺ has 21-fold higher activity than Mg²⁺. Six different DNAzymes are then tested, and only the DNAzymes active with Mg²⁺ (17E, 8–17, and E5) are active with Fe²⁺. Fe²⁺ has 25 and one- to twofold higher activity than Mg²⁺ for the 8–17 and E5 DNAzymes, respectively. In pH>7 buffer and in presence of air, 1 mm Fe²⁺ results in a nonspecific degradation of the DNA strand due to reactive oxygen species (ROS). Cleavage reactions in anoxic environment and antioxidant ascorbate can be used to overcome the effect of oxidation. The findings provide insights for potential DNAzyme catalysis in the early Earth, and they further support the similarity between Mg²⁺ and Fe²⁺ in enzyme catalysis.     

Multimedia content recommendation in social networks using mood tags and synonyms

Multimedia Systems - The preferences of Web information purchasers are changing. Cost-effectiveness (i.e., an emphasis on performance with respect to price) is becoming less regarded than...     

A multi-material part design framework in additive manufacturing

Additive manufacturing (AM) technologies provide more freedom to functional part design in various industries. One of the unique capabilities of AM is that multi-material parts can be produced with material compositional and geometric complexity. Multi-material parts have the advantage of achieving multiple performance requirements. In the research, we propose a framework for designing multi-material parts using AM processes. The proposed framework is composed of four interacting modules, including design requirement identification, primary material selection, AM process selection, material composition, and part geometry determination. Rules and guidelines for AM are integrated into the proposed framework with AM processes’ capabilities and constraints compiled in databases. We also introduce databases to assist in decision-making and ensure manufacturability of the designed multi-material part in various product design phases. The proposed framework is applied to a case study involving a conceptual design of a multi-material battery pack cooling plate.     

Inconel 617 alloy creep life augmentation by graphene transfer coating

Journal of Mechanical Science and Technology - Presently, countries are trying to increase their energy efficiency. Therefore, the operating temperatures of very high temperature reactor (VHTR) and...     

In situ estimation of equivalent porosity and strength of aluminum die-casting floor panel by simple loading test and finite element method

Journal of Mechanical Science and Technology - A simple estimation method of equivalent porosity and strength of scrapped aluminum die-casting floor panel is suggested in which various defects like...     

Charge Transportation and Chirality in Liquid Crystalline Helical Network Phases of Achiral BTBT-Derived Polycatenar Molecules

First examples of multichain (polycatenar) compounds, based on the π-conjugated [1]benzothieno[3,2-b]benzothiophene unit are designed, synthesized, and their soft self-assembly and charge carrier mobility are investigated. These compounds, terminated by the new fan-shaped 2-brominated 3,4,5-trialkoxybenzoate moiety, form bicontinuous cubic liquid crystalline (LC) phases with helical network structure over extremely wide temperature ranges (>200 K), including ambient temperature. Compounds with short chains show an achiral cubic phase with the double network, which upon increasing the chain length, is at first replaced by a tetragonal 3D phase and then by a mirror symmetry is broken triple network cubic phase. In the networks, the capability of bypassing defects provides enhanced charge carrier mobility compared to imperfectly aligned columnar phases, and the charge transportation is non-dispersive, as only rarely observed for LC materials. At the transition to a semicrystalline helical network phase, the conductivity is further enhanced by almost one order of magnitude. In addition, a mirror symmetry broken isotropic liquid phase is formed beside the 3D phases, which upon chain elongation is removed and replaced by a hexagonal columnar LC phase.     

Machine Learning-Based Two-Stage Data Selection Scheme for Long-Term Influenza Forecasting

One popular strategy to reduce the enormous number of illnesses and deaths from a seasonal influenza pandemic is to obtain the influenza vaccine on time. Usually, vaccine production preparation must be done at least six months in advance, and accurate long-term influenza forecasting is essential for this. Although diverse machine learning models have been proposed for influenza forecasting, they focus on short-term forecasting, and their performance is too dependent on input variables. For a country’s long-term influenza forecasting, typical surveillance data are known to be more effective than diverse external data on the Internet. We propose a two-stage data selection scheme for worldwide surveillance data to construct a long-term forecasting model for influenza in the target country. In the first stage, using a simple forecasting model based on the country’s surveillance data, we measured the change in performance by adding surveillance data from other countries, shifted by up to 52 weeks. In the second stage, for each set of surveillance data sorted by accuracy, we incrementally added data as input if the data have a positive effect on the performance of the forecasting model in the first stage. Using the selected surveillance data, we trained a new long-term forecasting model for influenza and perform influenza forecasting for the target country. We conducted extensive experiments using six machine learning models for the three target countries to verify the effectiveness of the proposed method. We report some of the results.