Scalable Synthesis of Triple‐Core–Shell Nanostructures of TiO2@MnO2@C for High Performance Supercapacitors Using Structure‐Guided Combustion Waves

Core–shell nanostructures of metal oxides and carbon‐based materials have emerged as outstanding electrode materials for supercapacitors and batteries. However, their synthesis requires complex procedures that incur high costs and long processing times. Herein, a new route is proposed for synthesizing triple‐core–shell nanoparticles of TiO₂@MnO₂@C using structure‐guided combustion waves (SGCWs), which originate from incomplete combustion inside chemical‐fuel‐wrapped nanostructures, and their application in supercapacitor electrodes. SGCWs transform TiO₂ to TiO₂@C and TiO₂@MnO₂ to TiO₂@MnO₂@C via the incompletely combusted carbonaceous fuels under an open‐air atmosphere, in seconds. The synthesized carbon layers act as templates for MnO₂ shells in TiO₂@C and organic shells of TiO₂@MnO₂@C. The TiO₂@MnO₂@C‐based electrodes exhibit a greater specific capacitance (488 F g^?1 at 5 mV s^?1) and capacitance retention (97.4% after 10 000 cycles at 1.0 V s^?1), while the absence of MnO₂ and carbon shells reveals a severe degradation in the specific capacitance and capacitance retention. Because the core‐TiO₂ nanoparticles and carbon shell prevent the deformation of the inner and outer sides of the MnO₂ shell, the nanostructures of the TiO₂@MnO₂@C are preserved despite the long‐term cycling, giving the superior performance. This SGCW‐driven fabrication enables the scalable synthesis of multiple‐core–shell structures applicable to diverse electrochemical applications.     

Effects of sintering conditions on the microstructure and mechanical properties of SiC prepared using powders recovered from kerf loss sludge

The effects of sintering conditions on the microstructure and mechanical properties of the sintered SiC prepared using the SiC powder recovered from the kerf loss sludge were investigated. The recovered SiC powders were consolidated by spark plasma sintering (SPS) and conventional sintering methods. The effects of sintering temperature, time and methods (SPS and conventional sintering) on the phase, grain size and density of SiC were systematically studied. The Vickers hardness of spark plasma-sintered (SPSed) samples was higher than that of conventional sintered samples due to small grain size. When holding time was increased from 10 to 30 min, the grain size and relative density of SPSed samples were also increased, which lead to the almost constant Vickers hardness by competing effects of grain size and relative density. When holding time was over 30 min, no appreciable change of the relative density and grain size were observed, which can lead to similar values of Vickers hardness. SPS process can be used to make SiC with high density and hardness at relatively low temperature compared with the conventional sintering process.     

Advanced Materials and Systems for Biodegradable,Transient Electronics

Transient electronics refers to an emerging class of advanced technology, defined by an ability to chemically or physically dissolve, disintegrate, and degrade in actively or passively controlled fashions to leave environmentally and physiologically harmless by-products in environments, particularly in bio-fluids or aqueous solutions. The unusual properties that are opposite to operational modes in conventional electronics for a nearly infinite time frame offer unprecedented opportunities in research areas of eco-friendly electronics, temporary biomedical implants, data-secure hardware systems, and others. This review highlights the developments of transient electronics, including materials, manufacturing strategies, electronic components, and transient kinetics, along with various potential applications.     

UFC-Net with Fully-Connected Layers and Hadamard Identity Skip Connection for Image Inpainting

Image inpainting is an interesting technique in computer vision and artificial intelligence for plausibly filling in blank areas of an image by referring to their surrounding areas. Although its performance has been improved significantly using diverse convolutional neural network (CNN)-based models, these models have difficulty filling in some erased areas due to the kernel size of the CNN. If the kernel size is too narrow for the blank area, the models cannot consider the entire surrounding area, only partial areas or none at all. This issue leads to typical problems of inpainting, such as pixel reconstruction failure and unintended filling. To alleviate this, in this paper, we propose a novel inpainting model called UFC-net that reinforces two components in U-net. The first component is the latent networks in the middle of U-net to consider the entire surrounding area. The second component is the Hadamard identity skip connection to improve the attention of the inpainting model on the blank areas and reduce computational cost. We performed extensive comparisons with other inpainting models using the Places2 dataset to evaluate the effectiveness of the proposed scheme. We report some of the results.     

DISTINÏCT: Data poISoning atTacks dectectIon usiNg optÏmized jaCcarddisTance

Machine Learning (ML) systems often involve a re-training process to make better predictions and classifications. This re-training process creates a loophole and poses a security threat for ML systems. Adversaries leverage this loophole and design data poisoning attacks against ML systems. Data poisoning attacks are a type of attack in which an adversary manipulates the training dataset to degrade the ML system’s performance. Data poisoning attacks are challenging to detect, and even more difficult to respond to, particularly in the Internet of Things (IoT) environment. To address this problem, we proposed DISTINÏCT, the first proactive data poisoning attack detection framework using distance measures. We found that Jaccard Distance (JD) can be used in the DISTINÏCT (among other distance measures) and we finally improved the JD to attain an Optimized JD (OJD) with lower time and space complexity. Our security analysis shows that the DISTINÏCT is secure against data poisoning attacks by considering key features of adversarial attacks. We conclude that the proposed OJD-based DISTINÏCT is effective and efficient against data poisoning attacks where in-time detection is critical for IoT applications with large volumes of streaming data.     

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.