A study on effects of security risks on acceptance of enterprise cloud service: moderating of employment and non-employment using PLS multiple group analysis

This paper analyzed the effects of security risk factors fit for cloud computing paradigm on the acceptance of enterprise cloud service with intent to illuminate the factors for vitalizing the adoption of corporate cloud services in the future. The acceptance intention was set as a dependent variable. Independent variables were set in reference to the technology acceptance theory. Security risks were categorized into compliance risk, information leakage risk, troubleshooting risk and service discontinuation risk to design a model for analysis.     

From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.     

Human interaction discovery in smartphone proximity networks

Since humans are fundamentally social beings and interact frequently with others in their daily life, understanding social context is of primary importance in building context-aware applications. In this paper, using smartphone Bluetooth as a proximity sensor to create social networks, we present a probabilistic approach to mine human interaction types in real life. Our analysis is conducted on Bluetooth data continuously sensed with smartphones for over one year from 40 individuals who are professionally or personally related. The results show that the model can automatically discover a variety of social contexts. We objectively validated our model by studying its predictive and retrieval performance.     

Theoretical justification of approximate norm minimization method for intelligent digital redesign

This paper investigates the theoretical justification of intelligent digital redesign (IDR) methods based on the approximate discrete-time models. Its main purpose is to design the digital fuzzy controller achieving the minimum norm distance between the trajectories of the approximate closed-loop systems under the analog and the digital controls. It is verified that, if there exist the digital gains such that the approximate norm distance is sufficiently small, the redesigned controller makes the trajectories of the exact closed-loop systems close, or, at least, asymptotically stabilizes the plant.     

Bio-Inspired Artificial Fast-Adaptive and Slow-Adaptive Mechanoreceptors With Synapse-Like Functions

Development of artificial mechanoreceptors capable of sensing and pre-processing external mechanical stimuli is a crucial step toward constructing neuromorphic perception systems that can learn and store information. Here, bio-inspired artificial fast-adaptive (FA) and slow-adaptive (SA) mechanoreceptors with synapse-like functions are demonstrated for tactile perception. These mechanoreceptors integrate self-powered piezoelectric pressure sensors with synaptic electrolyte-gated field-effect transistors (EGFETs) featuring a reduced graphene oxide channel. The FA pressure sensor is based on a piezoelectric poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) thin film, while the SA pressure sensor is enabled by a piezoelectric ionogel with the piezoelectric-ionic coupling effect based on P(VDF-TrFE) and an ionic liquid. Changes in post-synaptic current are achieved through the synaptic effect of the EGFET by regulating the amplitude, number, duration, and frequency of tactile stimuli (pre-synaptic pulses). These devices have great potential to serve as artificial biological mechanoreceptors for future artificial neuromorphic perception systems.     

GaP/GaNP Heterojunctions for Efficient Solar‐Driven Water Oxidation

The growth and characterization of an n‐GaP/i‐GaNP/p⁺‐GaP thin film heterojunction synthesized using a gas‐source molecular beam epitaxy (MBE) method, and its application for efficient solar‐driven water oxidation is reported. The TiO₂/Ni passivated n‐GaP/i‐GaNP/p⁺‐GaP thin film heterojunction provides much higher photoanodic performance in 1 m KOH solution than the TiO₂/Ni‐coated n‐GaP substrate, leading to much lower onset potential and much higher photocurrent. There is a significant photoanodic potential shift of 764 mV at a photocurrent of 0.34 mA cm^?2, leading to an onset potential of ≈0.4 V versus reversible hydrogen electrode (RHE) at 0.34 mA cm^?2 for the heterojunction. The photocurrent at the water oxidation potential (1.23 V vs RHE) is 1.46 and 7.26 mA cm^?2 for the coated n‐GaP and n‐GaP/i‐GaNP/p⁺‐GaP photoanodes, respectively. The passivated heterojunction offers a maximum applied bias photon‐to‐current efficiency (ABPE) of 1.9% while the ABPE of the coated n‐GaP sample is almost zero. Furthermore, the coated n‐GaP/i‐GaNP/p⁺‐GaP heterojunction photoanode provides a broad absorption spectrum up to ≈620 nm with incident photon‐to‐current efficiencies (IPCEs) of over 40% from ≈400 to ≈560 nm. The high low‐bias performance and broad absorption of the wide‐bandgap GaP/GaNP heterojunctions render them as a promising photoanode material for tandem photoelectrochemical (PEC) cells to carry out overall solar water splitting.     

Formation of a Surficial Bifunctional Nanolayer on Nb2O5 for Ultrastable Electrodes for Lithium‐Ion Battery

Safe and long cycle life electrode materials for lithium‐ion batteries are significantly important to meet the increasing demands of rechargeable batteries. Niobium pentoxide (Nb₂O₅) is one of the highly promising candidates for stable electrodes due to its safety and minimal volume expansion. Nevertheless, pulverization and low conductivity of Nb₂O₅ have remained as inherent challenges for its practical use as viable electrodes. A highly facile method is proposed to improve the overall cycle retention of Nb₂O₅ microparticles by ammonia (NH₃) gas‐driven nitridation. After nitridation, an ultrathin surficial layer (2 nm) is formed on the Nb₂O₅, acting as a bifunctional nanolayer that allows facile lithium (Li)‐ion transport (10–100 times higher Li diffusivity compared with pristine Nb₂O₅ microparticles) and further prevents the pulverization of Nb₂O₅. With the subsequent decoration of silver (Ag) nanoparticles (NPs), the low electric conductivity of nitridated Nb₂O₅ is also significantly improved. Cycle retention is greatly improved for nitridated Nb₂O₅ (96.7%) compared with Nb₂O₅ (64.7%) for 500 cycles. Ag‐decorated, nitridated Nb₂O₅ microparticles and nitridated Nb₂O₅ microparticles exhibit ultrastable cycling for 3000 cycles at high current density (3000 mA g^?1), which highlights the importance of the surficial nanolayer in improving overall electrochemical performances, in addition to conductive NPs.