Over the last few years, we have witnessed an exponential increase in the computing and storage capabilities of smart devices that has led to the popularity of an emerging technology called edge ...computing. Compared to the traditional cloud-computing- based infrastructure, computing and storage facilities are available near end users in edge computing. Moreover, with the widespread popularity of unmanned aerial vehicles (UAVs), huge amounts of information will be shared between edge devices and UAVs in the coming years. In this scenario, traffic surveillance using UAVs and edge computing devices is expected to become an integral part of the next generation intelligent transportation systems. However, surveillance in ITS requires uninterrupted data sharing, cooperative decision making, and stabilized network formation. Edge computing supports data processing and analysis closer to the deployed machines (i.e., the sources of the data). Instead of simply storing data and missing the opportunity to capitalize on it, edge devices can analyze data to gain insights before acting on them. Transferring data from the vehicle to the edge for real-time analysis can be facilitated by the use of UAVs, which can act as intermediate aerial nodes between the vehicles and edge nodes. However, as the communication between UAVs and edge devices is generally done using an open channel, there is a high risk of information leakage in this environment. Keeping our focus on all these issues, in this article, we propose a data-driven transportation optimization model where cyber-threat detection in smart vehicles is done using a probabilistic data structure (PDS)- based approach. A triple Bloom filter PDS- based scheduling technique for load balancing is initially used to host the real-time data coming from different vehicles, and then to distribute/collect the data to/from edges in a manner that minimizes the computational effort. The results obtained show that the proposed system requires comparatively less computational time and storage for load sharing, authentication, encryption, and decryption of data in the considered edge-computing-based smart transportation framework.
Two template removal methods were employed to create porosity in mesoporous silica SBA-15: ethanol extraction versus conventional high-temperature calcination. The resulting silicas were subjected to ...amine (3-aminopropyl) grafting and studied for their CO2 adsorption properties. The goal was to significantly increase the surface silanol density, and hence the grafted amine loading, leading directly to increased CO2 adsorption capacity and CO2/N2 selectivity. Thus, the silanol density was increased from 3.4 OH/nm2 for the calcined SBA-15 to 8.5 OH/nm2 for the SBA-15 by solvent extraction. Correspondingly, for these two samples, the grafted amine loading was increased from 2.2 to 3.2 mmol/g, and the CO2 adsorption capacity was increased from 1.05 to 1.6 mmol/g at conditions relevant to CO2 capture (0.15 bar and 25 °C), or a 52% increase. The CO2/N2 selectivity was increased from 46 to 131. The isosteric heats of adsorption, the sorbent stability during cyclic adsorption–desorption, and the (positive) effects of moisture on CO2 adsorption were also investigated and compared.
Along with the advancement of several emerging computing paradigms and technologies, such as cloud computing, mobile computing, artificial intelligence, and big data, Internet of Things (IoT) ...technologies have been applied in a variety of fields. In particular, the Internet of Healthcare Things (IoHT) is becoming increasingly important in human activity recognition (HAR) due to the rapid development of wearable and mobile devices. In this article, we focus on the deep-learning-enhanced HAR in IoHT environments. A semisupervised deep learning framework is designed and built for more accurate HAR, which efficiently uses and analyzes the weakly labeled sensor data to train the classifier learning model. To better solve the problem of the inadequately labeled sample, an intelligent autolabeling scheme based on deep <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula>-network (DQN) is developed with a newly designed distance-based reward rule which can improve the learning efficiency in IoT environments. A multisensor based data fusion mechanism is then developed to seamlessly integrate the on-body sensor data, context sensor data, and personal profile data together, and a long short-term memory (LSTM)-based classification method is proposed to identify fine-grained patterns according to the high-level features contextually extracted from the sequential motion data. Finally, experiments and evaluations are conducted to demonstrate the usefulness and effectiveness of the proposed method using real-world data.
We report on the first systematic study of spin transport in bilayer graphene (BLG) as a function of mobility, minimum conductivity, charge density, and temperature. The spin-relaxation time τ(s) ...scales inversely with the mobility μ of BLG samples both at room temperature (RT) and at low temperature (LT). This indicates the importance of D'yakonov-Perel' spin scattering in BLG. Spin-relaxation times of up to 2 ns at RT are observed in samples with the lowest mobility. These times are an order of magnitude longer than any values previously reported for single-layer graphene (SLG). We discuss the role of intrinsic and extrinsic factors that could lead to the dominance of D'yakonov-Perel' spin scattering in BLG. In comparison to SLG, significant changes in the carrier density dependence of τ(s) are observed as a function of temperature.
Gas adsorption experiments have been carried out on a zinc benzenetribenzoate metal−organic framework material, MOF-177. Hydrogen adsorption on MOF-177 at 298 K and 10 MPa gives an adsorption ...capacity of ∼0.62 wt %, which is among the highest hydrogen storage capacities reported in porous materials at ambient temperatures. The heats of adsorption for H2 on MOF-177 were −11.3 to −5.8 kJ/mol. By adding a H2 dissociating catalyst and using our bridge building technique to build carbon bridges for hydrogen spillover, the hydrogen adsorption capacity in MOF-177 was enhanced by a factor of ∼2.5, to 1.5 wt % at 298 K and 10 MPa, and the adsorption was reversible. N2 and O2 adsorption measurements showed that O2 was adsorbed more favorably than N2 on MOF-177 with a selectivity of ∼1.8 at 1 atm and 298 K, which makes MOF-177 a promising candidate for air separation. The isotherm was linear for O2 while being concave for N2. Water vapor adsorption studies indicated that MOF-177 adsorbed up to ∼10 wt % H2O at 298 K. The framework structure of MOF-177 was not stable upon H2O adsorption, which decomposed after exposure to ambient air in 3 days. All the results suggested that MOF-177 could be a potentially promising material for gas separation and storage applications at ambient temperature (under dry conditions or with predrying).
Local determinations of the Hubble constant H0 favor a higher value than Planck based on cosmic microwave background and Λ cold dark matter (Λ CDM). Through a model-independent expansion, we show ...that low redshift (z ≲ 0.7) data comprising baryon acoustic oscillations, cosmic chronometers, and Type Ia supernovae have a preference for quintessence models that lower H0 relative to Λ CDM . In addition, we confirm that an exponential coupling to dark matter cannot alter this conclusion in the same redshift range. Our results leave open the possibility that a coupling in the matter-dominated epoch, potentially even in the dark ages, may yet save H0 from sinking in the string theory swampland.
Endoplasmic reticulum (ER)-plasma membrane (PM) junctions are highly conserved subcellular structures. Despite their importance in Ca2+ signaling and lipid trafficking, the molecular mechanisms ...underlying the regulation and functions of ER-PM junctions remain unclear. By developing a genetically encoded marker that selectively monitors ER-PM junctions, we found that the connection between ER and PM was dynamically regulated by Ca2+ signaling. Elevation of cytosolic Ca2+ triggered translocation of E-Syt1 to ER-PM junctions to enhance ER-to-PM connection. This subsequently facilitated the recruitment of Nir2, a phosphatidylinositol transfer protein (PITP), to ER-PM junctions following receptor stimulation. Nir2 promoted the replenishment of PM phosphatidylinositol 4,5-bisphosphate (PIP2) after receptor-induced hydrolysis via its PITP activity. Disruption of the enhanced ER-to-PM connection resulted in reduced PM PIP2 replenishment and defective Ca2+ signaling. Altogether, our results suggest a feedback mechanism that replenishes PM PIP2 during receptor-induced Ca2+ signaling via the Ca2+ effector E-Syt1 and the PITP Nir2 at ER-PM junctions.
Display omitted
•A marker is developed for studying the regulation and functions of ER-PM junctions•Ca2+-induced E-Syt1 translocation to ER-PM junctions enhances ER-PM connection•An enhanced ER-PM connection facilitates Nir2 recruitment to ER-PM junctions•Nir2 promotes PM PIP2 replenishment following receptor-induced hydrolysis
The regulation and functions of endoplasmic reticulum (ER)-plasma membrane (PM) junctions are poorly understood. By developing a marker for ER-PM junctions, Liou and colleagues show that ER-PM connection is enhanced during Ca2+ signaling by E-Syt1. E-Syt1-mediated enhanced ER-PM connection facilitates the recruitment of a phosphatidylinositol transfer protein Nir2 to ER-PM junctions, resulting in replenishment of PM phosphatidylinositol 4,5-bisphosphate (PIP2) following receptor-induced hydrolysis. The study reveals a feedback mechanism at ER-PM junctions that replenishes PM PIP2 during receptor-induced Ca2+ signaling.
A data-based matched-mode source localization method is proposed in this paper for a moving source, using mode wavenumbers and depth functions estimated directly from the data, without requiring any ...environmental acoustic information and assuming any propagation model. The method is in theory free of the environmental mismatch problem because the mode replicas are estimated from the same data used to localize the source. Besides the estimation error due to the approximations made in deriving the data-based algorithms, the method has some inherent drawbacks: (1) It uses a smaller number of modes than theoretically possible because some modes are not resolved in the measurements, and (2) the depth search is limited to the depth covered by the receivers. Using simulated data, it is found that the performance degradation due to the afore-mentioned approximation/limitation is marginal compared with the original matched-mode source localization method. The proposed method has a potential to estimate the source range and depth for real data and be free of the environmental mismatch problem, noting that certain aspects of the (estimation) algorithms have previously been tested against data. The key issues are discussed in this paper.
In this letter, we characterize the electrical properties of commercial bulk 40-nm MOSFETs at room and deep cryogenic temperatures, with a focus on quantum information processing (QIP) applications. ...At 50 mK, the devices operate as classical FETs or quantum dot devices when either a high or low drain bias is applied, respectively. The operation in classical regime shows improved transconductance and subthreshold slope with respect to 300 K. In the quantum regime, all measured devices show Coulomb blockade. This is explained by the formation of quantum dots in the channel, for which a model is proposed. The variability in parameters, important for quantum computing scaling, is also quantified. Our results show that bulk 40-nm node MOSFETs can be readily used for the co-integration of cryo-CMOS classical-quantum circuits at deep cryogenic temperatures and that the variability approaches the uniformity requirements to enable shared control.
PDF
