Understanding the influence of water layers adjacent to interfaces is fundamental in order to fully comprehend the interactions of both biological and nonbiological materials in aqueous environments. ...In this study, we have investigated hydration forces at the mica–electrolyte interface as a function of ion valency and concentration using subnanometer oscillation amplitude frequency modulation atomic force microscopy (FM-AFM). Our results reveal new insights into the nature of hydration forces at interfaces due to our ability to measure high force gradients without instability and in the absence of lateral confinement due to the use of an atomically sharp tip. We demonstrate the influence of electrolytes on the properties of both primary and structural hydration forces and reveal new insights into the interplay between these phenomena in determining the interaction forces experienced by a nanoscale object approaching an interface. We also highlight the difficulty in directly comparing hydration force data from different measurement techniques where the nature of the perturbation induced by differing interaction geometries is likely to dramatically affect the results.
Local ionic environments within nanometer proximity of a surface play a major role in the interactions which occur there and can be of critical importance in, for example, colloid suspensions, as ...well as biological function. Such environments often vary significantly from bulk properties, as we show here by the direct imaging of a range of monovalent (Li+, Na+) and divalent (Ca2+, Mg2+) cations distributed at the liquid−solid interface of mica. We image local charge distributions relative to the atomic lattice of mica and adjacent structured water and explain how their location is influenced by the electrostatic characteristics of the underlying lattice.
This study aimed to provide a comprehensive analysis of the factors that determine and shape consumers’ behavioral intention to adopt mobile commerce (m-commerce). By integrating the core constructs ...from the Unified Theory of Acceptance and Use of Technology (UTAUT), together with the trust-building mechanisms, this study explored the importance of the institutional mechanisms and their moderating effects between trust in the vendors and intention to adopt m-commerce. Traditionally, the effects of institutional mechanisms on trust and adoption intention have been considered separately in different study contexts. The purpose of this study was to extend the literature by simultaneously exploring two institutional mechanisms that are conceptually highly similar to each other, namely, structural assurance (SA) and perceived effectiveness of e-commerce institutional mechanisms (PEEIM). A self-administered survey was used to collect data, which were analyzed using partial least squares structural equation modelling (PLS-SEM). The results revealed that most of the constructs examined have significant relationships with the intention to adopt m-commerce. Additionally, PEEIM exhibits a significant moderating effect but SA does not. This study delineates how trust-building mechanisms play important roles in increasing consumers’ confidence in order to promote m-commerce adoption.
We present the design of an Application Specific Integrated Circuit (ASIC) digital clock based on the 0.12 µm deep submicron technology node. The widths of the PMOS and NMOS transistors are 0.72 µm ...and 0.24 µm, respectively. The clock expresses time based on the 12-hour time notation. The gate-level schematic and the layout of the design are drawn and validated using DSCH3 and Microwind3 Lite. The key feature of the clock is constructed from 18 D-type flip-flops. Two modulo-60 counters and a modulo-12 counter are built from the flip-flops. The modulo-60 counters are used for the second and minute modules, while the modulo-12 flip-flop is for the hour module. The length and width of the layout are, respectively, 153.60 µm and 58.14 µm. This is to say that the size of the die is comparable with that of a human hair. The average static power dissipation is found to be 0.202 mW, which is reasonably low. Since the proposed design is in the form of an ASIC chip, the input and output pins merely require to be connected to an external power source, an oscillator, and displays, to allow the clock to operate properly. With its miniaturized size and low power consumption, the proposed design clearly exhibits advantages over those built using discrete components and general-purpose chips.
A deep-learning-based approach for recognizing integrated circuit (IC) packaging type is presented in this paper. The objective of this work is to design a deep-learning method that can recognize ...multiple types of packaging per detection, performing counting operations, and calculating the centre location of an IC with its tilting angle. The transfer learning from model You-Only-Look-Once (YOLO) v5 was chosen because it has been trained with the coco dataset and has a more reliable feature extraction system than the other models. In order to extract data from images, OpenCV was used, which allows the deep learning model to perform more efficient analysis of the input data. Apart from that, the principal component analysis (PCA) was used to estimate the angle of the IC in order to determine the rotation of each IC for the purpose of tilting adjustment. The developed model has an average confidence score of 85% and is capable of operating in a variety of conditions, as demonstrated by ANOVA analysis.
Atomic force microscopy (AFM) has been used extensively in nanoscience research since its invention. Recently, many teaching laboratories in colleges, undergraduate institutions, and even high ...schools incorporate AFM as an effective teaching tool for nanoscience education. This paper presents an optical beam deflection (OBD) based atomic force microscope, designed specifically for the undergraduate engineering laboratory as a teaching instrument. An electronic module for signal conditioning was built with components that are commonly available in an undergraduate electronic laboratory. In addition to off-the-shelf mechanical parts and optics, the design of custom-built mechanical parts waskept as simple as possible. Hence, the overall cost for the setup is greatly reduced. The AFM controller was developed using National Instruments Educational Laboratory Virtual Instrumentation Suite (NI ELVIS), an integrated hardware and software platform which can be programmed in LabVIEW. A simple yet effective control algorithm for scanning and feedback control was developed. Despite the use of an educational platform and low-cost components from the undergraduate laboratory, the developed AFM is capable of performing imaging in constant-force mode with submicron resolution and at reasonable scanning speed (approximately 18 min per image). Therefore, the AFM is suitable to be used as an educational tool for nanoscience. Moreover, the construction of the system can be a valuable educational experience for electronic and mechanical engineering students.
An approach for recognizing and decoding the industrial-based dot peen data matrix code is presented in this paper. Dot peen marking is a type of direct part marking (DPM). Due to the reduced ...contrast characteristic, it could be difficult to read a DPM code. Additionally, the readability of a DPM code may deteriorate over time due to partial degradation on the product surface. A deep-learning-based method using You-Only-Look-Once (YOLO) v5 model is proposed. Firstly, a large dataset of dot peen data matrix symbols was prepared to initiate the YOLOv5 model training. Image data augmentation was then applied to the training images to increase the size of the training dataset. The YOLOv5 model training was processed with a batch size of 16 and the epochs number of 60 due to its high accuracy (97.79%). All dot peen data matrix codes were detected accurately within one second, fulfilling our intention to design a high-speed reader for industrial-based dot peen data matrix. With ANOVA analysis, we observed that the brightness level and the camera distance significantly affect the decoding process. Additionally, our developed model can successfully decode a partially damage code if the level of damage is below 30%.
The traffic congestion at the junction is becoming one of the major issues for many cities all around the world. One of the reasons causing this issue is due to the inefficient of the existing ...traffic light system at the traffic junction. This paper proposes a Smart Traffic Light Controller System (STLCS) with deep learning capability in image processing. The developed STLCS is comprised of Altera DE2 board, personal computer and Intel Neural Compute Stick 2 (NCS2). The personal computer is used as the vehicle detection system of the STLCS by performing various computer vision tasks and inference. The tasks include image acquisition, processing, and vehicle detection and counting. The smart feature of the system can detect the vehicles by using deep learning model and compute a flexible green time for each lane according to the density of traffic in each lane. The vehicle detection emphasizes the image processing by using the deep learning algorithm from the pre-trained model to increase the efficiency and computing time of the system. The efficiency of the vehicle detection system is about 94.73%.
Higher education institutions worldwide have been greatly affected by the COVID-19 pandemic about two years ago. As a consequence, learning and teaching mode were then changed into online platform. ...Today, even though most of the class activities have resumed back to physical mode, online learning is still remained as an important platform for learning. Apart from desktop computers and laptops, students use smartphones considerably for online learning. However, the physical characteristics of a smartphone could hinder its ability to work as an effective tool for academic task. In the context of online learning, this study aims to explore the adoption of an advanced feature of a modern smartphone: the desktop mode, which could possibly overcome the physical limitations of a standard smartphone. The factors that influence the undergraduate students’ intention to use the smartphone’s desktop mode for online learning were examined. By using constructs from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), Learning Value (LV) and Empowerment in Learning (EiL), the PLS-SEM method was used to analyze the data collected with a self-administered survey. The results revealed that Performance Expectancy, Social Influence, Hedonic Motivation, Habit and EiL positively influence students’ behavioral intention (BI) to use smartphone’s desktop mode in online learning. Additionally, gender was found to have moderating effects on the relationship between some constructs and BI.
PDF
