Breathing-Driven Self-Powered Pyroelectric ZnO Integrated Face Mask for Bioprotection

Rapid spread of infectious diseases is a global threat and has an adverse impact on human health, livelihood, and economic stability, as manifested in the ongoing coronavirus disease 2019 (COVID-19) pandemic. Even though people wear a face mask as protective equipment, direct disinfection of the pathogens is barely feasible, which thereby urges the development of biocidal agents. Meanwhile, repetitive respiration generates temperature variation wherein the heat is regrettably wasted. Herein, a biocidal ZnO nanorod-modified paper (ZNR-paper) composite that is 1) integrated on a face mask, 2) harvests waste breathing-driven thermal energy, 3) facilitates the pyrocatalytic production of reactive oxygen species (ROS), and ultimately 4) exhibits antibacterial and antiviral performance is proposed. Furthermore, in situ generated compressive/tensile strain of the composite by being attached to a curved mask is investigated for high pyroelectricity. The anisotropic ZNR distortion in the bent composite is verified with changes in Zn O bond lengths and O Zn O bond angles in a ZnO₄ tetrahedron, resulting in an increased polarization state and possibly contributing to the following pyroelectricity. The enhanced pyroelectric behavior is demonstrated by efficient ROS production and notable bioprotection. This study exploring the pre-strain effect on the pyroelectricity of ZNR-paper might provide new insights into the piezo-/pyroelectric material-based applications.     

Cuproptosis-Inducible Chemotherapeutic/Cascade Catalytic Reactor System for Combating with Breast Cancer

Cascade hydroxyl radical generating hydrogel reactor structures including a chemotherapeutic agent are invented for multiple treatment of breast cancer. Glucose oxidase (GOx) and cupric sulfate (Cu) are introduced for transforming accumulated glucose (in cancer cells) to hydroxyl radicals for starvation/chemodynamic therapy. Cu may also suppress cancer cell growth via cuproptosis-mediated cell death. Berberine hydrochloride (BER) is engaged as a chemotherapeutic agent in the hydrogel reactor for combining with starvation/chemodynamic/cuproptosis therapeutic modalities. Moreover, Cu is participated as a gel crosslinker by coordinating with catechol groups in hyaluronic acid-dopamine (HD) polymer. Controlling viscoelasticity of hydrogel reactor can extend the retention time following local injection and provide sustained drug release patterns. Low biodegradation rate of designed HD/BER/GOx/Cu hydrogel can reduce dosing frequency in local cancer therapy and avoid invasiveness-related inconveniences. Especially, it is anticipated that HD/BER/GOx/Cu hydrogel system can be applied for reducing size of breast cancer prior to surgery as well as tumor growth suppression in clinical application.     

All about activity injection: Threats,semantics, detection,and defense

Android supports seamless user experience by maintaining activities from different applications (apps) in the same activity stack. Although such close inter-app communication is essential in the Android framework, the powerful inter-app communication contains vulnerabilities that can inject malicious activities into a victim app's activity stack to hijack user interaction flows. In this article, we demonstrate activity injection attacks with a simple malware, and formally specify the activity activation mechanism using operational semantics. Based on the operational semantics, we develop a static analysis tool, which analyzes Android apps to detect activity injection attacks. Our tool is fast enough to analyze real-world Android apps in 6 seconds on average, and our experiments found that 1761 apps out of 129,756 real-world Android apps inject their activities into other apps' tasks. Moreover, we propose a defense mechanism, dubbed signature-based activity access control (SAAC), which completely prohibits activity injection attacks. The defense mechanism is general enough to keep the current Android multitasking features intact, and it is simple enough to be independent of the complex activity activation semantics, which does not increase activity activation time noticeably. With the extension of the formal semantics for SAAC, we prove that SAAC correctly mitigates activity injection attacks without any false alarms.     

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.