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.     

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.