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The escalating complex air pollution, including particulate matter (PM) and volatile organic compounds caused by rapid industrialization and urbanization, has necessitated diverse customized air filtration systems for individuals or buildings. Herein, a customized, in situ functionalized nanofiber filter fabricated by an all-in-one printing combining 3D printing and electrolyte-assisted electrospinning (ELES) is reported. In the ELES process, the electrolyte solution enables the electrospun nanofibers to be selectively deposited and self-assembled onto a non-conductive 3D framework at a designated position with pre-designed grooves. As a result, seamless integration without flaws between 3D frameworks and electrospun nanofibers, which show high PM filtration efficiency, is achieved. The all-in-one printing process demonstrates that the customized air filters perfectly fit the commercial air purifier, showing the design flexibility for customized air purifying applications. Lastly, the electrolyte solution can contain a formaldehyde-responsive agent, allowing the in situ functionalization of nanofiber filters. Thereby, a dual-functional customized nanofiber filter for capturing PM and sensing formaldehyde can be fabricated in a single device without manual intervention. Overall, this all-in-one printing marks a pivotal advancement in the fabrication of customized nanofiber filters with customizable designs and dual-functionality for diverse air purification applications to cope with complex air contamination scenarios.

Today very few deep learning-based mobile augmented reality (MAR) applications are applied in mobile devices because they are significantly energy-guzzling. In this paper, we design an edge-based energy-aware MAR system that enables MAR devices to dynamically change their configurations, such as CPU frequency, computation model size, and image offloading frequency based on user preferences, camera sampling rates, and available radio resources. Our proposed dynamic MAR configuration adaptations can minimize the per frame energy consumption of multiple MAR clients without degrading their preferred MAR performance metrics, such as latency and detection accuracy. To thoroughly analyze the interactions among MAR configurations, user preferences, camera sampling rate, and energy consumption, we propose, to the best of our knowledge, the first comprehensive analytical energy model for MAR devices. Based on the proposed analytical model, we design a LEAF optimization algorithm to guide the MAR configuration adaptation and server radio resource allocation. An image offloading frequency orchestrator, coordinating with the LEAF, is developed to adaptively regulate the edge-based object detection invocations and to further improve the energy efficiency of MAR devices. Extensive evaluations are conducted to validate the performance of the proposed analytical model and algorithms.

Autonomous driving perceives surroundings with line-of-sight sensors that are compromised under environmental uncertainties. To achieve real time global information in high definition map, we investigate to share perception information among connected and automated vehicles. However, it is challenging to achieve real time perception sharing under varying network dynamics in automotive edge computing. In this paper, we propose a novel real time dynamic map, named LiveMap to detect, match, and track objects on the road. We design the data plane of LiveMap to efficiently process individual vehicle data with multiple sequential computation components, including detection, projection, extraction, matching and combination. We design the control plane of LiveMap to achieve adaptive vehicular offloading with two new algorithms (central and distributed) to balance the latency and coverage performance based on deep reinforcement learning techniques. We conduct extensive evaluation through both realistic experiments on a small-scale physical testbed and network simulations on an edge network simulator. The results suggest that LiveMap significantly outperforms existing solutions in terms of latency, coverage, and accuracy.

In this study, Son et al. describe the functional and evolutionary significance of the N-terminal proline-rich disordered domain (PRD) of DROSHA, a key factor for miRNA processing. They show that the PRD preferentially facilitates the maturation of rapidly evolving, primary miRNA hairpins located within introns, thus providing new insights into regulation of miRNA biogenesis.

Transparent triboelectric nanogenerators (TENGs) are promising sustainable energy sources utilizable in optoelectronic devices. Here, we report a highly transparent TENG realized using AgNW-spray-deposition with a surfactant (ASDS). The ASDS process achieved coffee-ring-free AgNW with superior light transmittance of 87.6% and sheet resistance of 12.4 Ω/sq (ΦFOM= 21.5 × 10−3Ω−1), and possessed a high degree of design flexibility on 2D, 3D-curved substrates. The AgNW-spray-deposition with surfactant based transparent TENG (AT-TENG) with hydrophobic characteristics enables to generate electrical signals through solid–solid and solid–liquid contact electrification. As proof-of-concept applications, a self-powered bendable tactile sensor, which exploited biomechanical energy from finger-touch motion, and hybrid energy harvester to concurrently harvest solar and raindrop energy, are demonstrated. The self-powered bendable tactile sensor transformed the finger-touch into electrical energy recognized by the computer, and was utilized as a transparent keypad. The hybrid energy harvester, which is AT-TENG unified with a photovoltaic system, generates electricity from light and falling-droplets. The AT-TENG does not significantly degrade the output performance of photovoltaic system, and their parallel connection enables to achieve the maximum output voltage of 7.0 V. AT-TENGs have advantages such as simplicity of fabrication, outstanding transparency, and superior electrical performance, and have potential for future applications in wearable electronics and smart-home systems.

Emerging connected vehicular services, such as intelligent driving and high-definition (HD) map, are gaining increasing interest with the fast development of multi-access edge computing (MEC). For most time-sensitive and computation-intensive vehicular services, the data offloading process significantly influences the capacity and performance of MEC, especially when the number of connected vehicles is enormous. In this work, we consider data offloading optimization for a large-scale automotive MEC network. The problem is challenging due to the large number of connected vehicles and the complicated interaction between vehicles and edge servers. To tackle the scalability problem, we reformulate the original offloading optimization problem into a Mean-Field-Game (MFG) problem by abstracting the interaction among the connected vehicles as a distribution over their state spaces of task sizes, known as the mean-field term. To solve the problem efficiently, we propose a G-prox Primal-Dual-Hybrid-Gradient (PDHG) algorithm that transforms the MFG problem into a saddle-point problem. Based on our developed MFG model and G-prox PDHG algorithm, we propose the first data offloading scheme whose computation time is independent of the number of connected vehicles in automotive MEC systems. Extensive evaluation results corroborate the superior performance of our proposed scheme compared with the state-of-the-art methods.

As a good example of Advanced Driver-Assistance Systems (ADAS), Advisory Speed Assistance (ASA) helps improve driving safety and possibly energy efficiency by showing advisory speed to the driver of an intelligent vehicle. However, driver-based speed tracking errors often emerge, due to the perception and reaction delay, as well as imperfect vehicle control, degrading the effectiveness of ASA system. In this study, we propose a learning-based approach to modeling driver behavior, aiming to predict and compensate for the speed tracking errors in real time. Subject drivers are first classified into different types according to their driving behaviors using the k-nearest neighbors (k-NN) algorithm. A nonlinear autoregressive (NAR) neural network is then adopted to predict the speed tracking errors generated by each driver. A specific traffic scenario has been created in a Unity game engine-based driving simulator platform, where ASA system provides advisory driving speed to the driver via a head-up display (HUD). A human-in-the-loop simulation study is conducted by 17 volunteer drivers, revealing a 53% reduction in the speed error variance and a 3% reduction in the energy consumption with the compensation of the speed tracking errors. The results are further validated by a field implementation with a real passenger vehicle.

In harsh factory environment, achieving design and deployment of wearable sensor is challenging for foundation of industrial internet of things (IoT) systems. Herein, we introduce an all-aerosol-sprayed impact-resistant wearable triboelectric sensor (SC-WTS) that integrates SiO2nanoparticle-based shear thickening and a charge-storing (SC) interlayer. This interlayer synergistically enhances both anti-impact and self-powered sensing performances, enabling precise detection with a wide pressure range of 0.25–10 kPa, outstanding linearity of 0.96, and high sensitivity of 21.1 V/kPa. Fabricated using an all-aerosol-spray process, the SC-WTS is highly adaptable to diverse wearable substrates, including Kevlar, leather, and plastic plates, and exhibits excellent impact resistance against low-velocity impacts. By conjugating the in-situfabrication, outstanding self-powered sensing performance, and superior anti-impact performance, a real-time safety monitoring IIoT system was developed utilizing an industrial wireless server network and open platform communications unified architecture. The system enables supervisors to detect industrial collisions and emergency messages in real time through the triboelectric signals generated by the SC-WTS module, which is embedded in safety clothes of onsite workers. This work provides the practical concept of a WTS based on an SC interlayer with excellent anti-impact and self-powered sensing performance for wireless human-machine interfaces under extreme conditions at industrial sites.

Practical utilization of the triboelectric nanogenerators (TENGs), which have unique advantageous aspects of a simple operation mechanism, material selection diversity, and high energy conversion efficiency, requires high functionality as well as a scalable fabrication to broaden the applicable fields. Here, we report the concept of all-aerosol-sprayed transparent TENG (ST-TENG) by sequentially spraying total three layers of a silver nanowire conductive layer, transparent polystyrene charge-storage layer, and fluoropolymer contact layer. All layers were achieved by aerosol spray process, thereby exhibiting high design flexibility enough to fabricate ST-TENGs on various pre-made commercial devices. The presence of the transparent polystyrene layer as a charge storing interlayer significantly enhances the power density of the ST-TENG up to 818 mW/m2. As proof-of-concept applications, a self-powered invisible security internet-of-things system, which could blend with the surroundings, exploited biomechanical energy from foot-step for energy harvesting and invasion sensing. Furthermore, by exploiting the all-aerosol-spray process and high light transmittance of the ST-TENG, a hybrid energy harvester to simultaneously generate electricity from solar and rain energy was directly fabricated on a commercial solar panel. This work provides the brand-new concept of TENG with a scalable in-situ fabrication process, enhanced performance, and outstanding light transmittance.