Improving automated crisis detection via an improved understanding of crisis language: Linguistic categories in social media crises

By applying the Linguistic Category Model (LCM) in crisis communication, this study explores the potential of verb tracking on social media to examine how linguistic categories can elucidate the intentional and/or unintentional communication of crisis attribution frames. Through a content analysis, linguistic categories used in both media posts reporting three clusters of crisis and public comments on Facebook were examined. Results indicated that linguistic abstraction in both media post and public comments describing the crisis varied based on crisis cluster, suggesting that the level of linguistic abstraction reflected perceived attribution of responsibility through stability, locus and controllability. Language used to describe preventable crisis tend to be more abstract than those used to describe accidental and victim crisis. Findings of this study empirically tested the integration of LCM in crisis communication and implied potential application of LCM in building automated environmental scanning and crisis prediction systems.     

Variable selection for collaborative filtering with market basket data

The market basket data in the form of a binary user–item matrix or a binary item–user matrix can be modeled as a binary classification problem, which actually tackles collaborative filtering (CF) as well as target marketing. Effective variable selection (VS) can increase the prediction accuracy as well as identify important users or items in CF as well as target marketing. Therefore, we propose two new VS approaches: a Pearson correlation‐based approach and a forward random forests regression‐based approach, comparing the performance in a variety of experimental settings. The experimental results show that the proposed VS approaches outperform the conventional approaches in the examples. Furthermore, the experimental results are more reasonable and informative than the previous experimental results because the binary misclassification error and Top‐N accuracy for the user CF, the item CF, the user modeling, and the item modeling are all considered in this paper.     

Performance Enhancement of Hybrid TDOA/AOA Using Multipath Delay Estimation

Wireless Personal Communications - Wireless solar blind ultraviolet (UV) scattering communication is a new type of atmosphere optics communication technology with the important and potential...     

A New Data Layout Scheme for Energy-Efficient MapReduce Processing Tasks

Yet Another Resource Negotiator (YARN) is a framework to manage and allocate resource requests from applications that process big data stored in HDFS. However, dynamic power management methods are not efficient when YARN manage applications to process big data stored in the default data layout of HDFS. In this paper, we propose a new data layout scheme that can be implemented for HDFS. A comparison between our proposal and the existing HDFS data layout scheme shows that the new data layout algorithm significantly reduces the energy consumption at the slight expense of the mean response time of jobs.     

Facilitating application of language skills in authentic environments with a mobile learning system

We uncovered two critical issues in earlier studies: (a) some studies have shown that mobile learning technology is not beneficial for all students due to complexity of learning environments and student prior knowledge, skills, and experience and (b) familiarity of students with the authentic environments in which they learn using mobile technology did not receive much attention in earlier studies. To address these issues, we designed three learning tasks for a class of 26 junior high school students. The students applied language skills by completing the tasks in authentic environments individually in a first task, loosely collaborating with peers in a second task, and tightly collaborating with peers in a third task. A mobile learning system was also designed in this study to support students to accomplish the tasks. The aim of this study was to explore students' learning experiences using the learning system, their perceptions towards the system, and to assess how differently the students perform on the three tasks. According to our design, in the first task, the students took pictures of objects and described them orally or in writing using the mobile learning system. In the second task, after the students completed assignments, each student received comments from a partner through the system. In comments, the partner indicated flaws in student assignments and suggested how to fix them. In the third task, the students completed assignments, shared them using the system, and then exchanged comments with their partners face to face regarding issues related to their completed assignments and suggested how to improve them. Such learning behaviours in the three tasks enabled the students to practise writing and speaking skills. Our results demonstrate that most of the students highly valued our learning system and intend to use it in the future. Furthermore, the results show that the students performed best when they collaborated; namely, student performance was enhanced the most after the third task that required tight collaboration. Based on our results, we learned that students' familiarity with authentic environments is very important and beneficial for their learning. In addition, we learned that even in complex environment, less skilled and experienced students with low prior knowledge can perform well when they tightly collaborate with more skilled and experienced students with high prior knowledge, and our learning system can facilitate such collaboration.     

Distributed beamforming with one-bit feedback and clustering for multi-node wireless energy transfer

To resolve energy depletion issues in massive Internet of Things sensor networks, we developed a set of distributed energy beamforming methods with one-bit feedback and clustering for multi-node wireless energy transfer, where multiple single-antenna distributed energy transmitters (Txs) transfer their energy to multiple nodes wirelessly. Unlike previous works focusing on distributed information beamforming using a single energy receiver (Rx) node, we developed a distributed energy beamforming method for multiple Rx nodes. Additionally, we propose two clustering methods in which each Tx node chooses a suitable Rx node. Furthermore, we propose a fast distributed beamforming method based on Tx sub-clustering. Through computer simulations, we demonstrate that the proposed distributed beamforming method makes it possible to transfer wireless energy to massive numbers of sensors effectively and rapidly with small implementation complexity. We also analyze the energy harvesting outage probability of the proposed beamforming method, which provides insights into the design of wireless energy transfer networks with distributed beamforming.     

Digital Laser Micropainting for Reprogrammable Optoelectronic Applications

Structural coloration is closely related to the progress of innovative optoelectronic applications, but the absence of direct, on-demand, and rewritable coloration schemes has impeded advances in the relevant area, particularly including the development of customized, reprogrammable optoelectronic devices. To overcome these limitations, a digital laser micropainting technique, based on controlled thin-film interference, is proposed through direct growth of the absorbing metal oxide layer on a metallic reflector in the solution environment via a laser. A continuous-wave laser simultaneously performs two functions—a photothermal reaction for site-selective metal oxide layer growth and in situ real-time monitoring of its thickness—while the reflection spectrum is tuned in a broad visible spectrum according to the laser fluence. The scalability and controllability of the proposed scheme is verified by laser-printed painting, while altering the thickness via supplementary irradiation of the identical laser in the homogeneous and heterogeneous solutions facilitates the modification of the original coloration. Finally, the proof-of-concept bolometer device verifies that specific wavelength-dependent photoresponsivity can be assigned, erased, and reassigned by the successive application of the proposed digital laser micropainting technique, which substantiates its potential to offer a new route for reprogrammable optoelectronic applications.     

Highly Reliable Charge Trap-Type Organic Non-Volatile Memory Device Using Advanced Band-Engineered Organic-Inorganic Hybrid Dielectric Stacks

With the recent interest in data storage in flexible electronics, highly reliable charge trap-type organic-based non-volatile memory (CT-ONVM) has attracted much attention. CT-ONVM should have a wide memory window, good endurance, and long-term retention characteristics, as well as mechanical flexibility. This paper proposed CT-ONVM devices consisting of band-engineered organic–inorganic hybrid films synthesized via an initiated chemical vapor deposition process. The synthesized poly(1,3,5-trimethyl-1,3,5,-trivinyl cyclotrisiloxane) and Al hybrid films are used as a tunneling dielectric layer and a blocking dielectric layer, respectively. For the charge trapping layer, different Hf, Zr, and Ti hybrids are examined, and their memory performances are systematically compared. The best combination of hybrid dielectric stacks showed a wide memory window of 6.77 V, good endurance of up to 10⁴ cycles, and charge retention of up to 71% after 10⁸ s even under the 2% strained condition. The CT-ONVM device using the hybrid dielectric stacks outperforms other organic-based charge trap memory devices and is even comparable in performance to conventional inorganic-based poly-silicon/oxide/nitride/oxide/silicon structures devices. The CT-ONVM using hybrid dielectrics can overcome the inherent low reliability and process complexity limitations of organic electronics and expedite the realization of wearable organic electronics.     

Achieving Narrow FWHM and High EQE Over 38% in Blue OLEDs Using Rigid Heteroatom-Based Deep Blue TADF Sensitized Host

Simultaneously obtaining high efficiency and deep blue emission in organic light emitting diodes (OLEDs) remains a challenge. To overcome the demands associated with deep blue thermally activated delayed fluorescence (TADF) emitters, two deep blue TADF materials namely, DBA–BFICz and DBA–BTICz, are designed and synthesized by incorporating oxygen-bridged boron (DBA) acceptor with heteroatoms, oxygen and sulphur-based donors, BFICz and BTICz, respectively. Both TADF materials show deep blue photoluminescence emissions below 450 nm by enhancing the optical band gap over 2.8 eV through deeper highest occupied molecular orbital (HOMO) level of heteroatom based donor moieties. At the same time, the photoluminescence quantum yields (PLQYs) of both TADF materials remain over 94%. The TADF device with DBA–BFICz as an emitter exhibits a good external quantum efficiency (EQE) of 33.2%. Since both new TADF materials show deep blue emissions and high efficiencies, hyperfluorescence (HF) OLED devices are fabricated using ν-DABNA as a fluorescence dopant. DBA–BFICz as a TADF sensitized host in HF–OLED reveals an outstanding EQE of 38.8% along with narrow full width at half maximum of 19 nm in the bottom emission pure blue OLEDs. This study provides an approach to develop deep blue TADF emitters for highly efficient OLEDs.     

Enhanced Thermal Transport across Self-Interfacing van der Waals Contacts in Flexible Thermal Devices

Minimizing the thermal contact resistance (TCR) at the boundary between two bodies in contact is critical in diverse thermal transport devices. Conventional thermal contact methods have several limitations, such as high TCR, low interfacial adhesion, a requirement for high external pressure, and low optical transparency. Here, a self-interfacing flexible thermal device (STD) that can form robust van der Waals mechanical contact and low-resistant thermal contact to planar and non-planar substrates without the need for external pressure or surface modification is presented. The device is based on a distinctive integration of a bioinspired adhesive architecture and a thermal transport layer formed from percolating silver nanowire (AgNW) networks. The proposed device exhibits a strong attachment (maximum 538.9 kPa) to target substrates while facilitating thermal transport across the contact interface with low TCR (0.012 m² K kW⁻¹) without the use of external pressure, thermal interfacial materials, or surface chemistries.