Search

With its high area density, bit-patterned media recording (BPMR) is emerging as a leading technology for next-generation storage systems. However, as area density increases, magnetic islands are positioned closer together, causing significant two-dimensional (2D) interference. To address this, detection methods are used to interpret the received signal and mitigate 2D interference. Recently, the maximum a posteriori (MAP) detection algorithm has shown promise in improving BPMR performance, though it requires extrinsic information to effectively reduce interference. In this paper, to solve the 2D interference and improve the performance of BPMR systems, a model using low-density parity-check (LDPC) coding was introduced to supply the MAP detector with the needed extrinsic information, enhancing detection in a joint decoding model we call MAP–LDPC. Additionally, leveraging similarities between LDPC codes and graph neural networks (GNNs), we replace the traditional sum–product algorithm in LDPC decoding with a GNN, creating a new model, MAP–GNN. The simulation results demonstrate that MAP–GNN achieves superior performance, particularly when using the deep learning-based GNN approach over conventional techniques. [ABSTRACT FROM AUTHOR]

With the increasingly sophisticated improvements in artificial intelligence (AI) technologies in marketing communications, a gap prevails between heightened levels of excitement around it and high-level application. Consequently, this topic necessitates in-depth investigation to prepare for future changes. This study examines the role of AI in programmatic advertising through a quantitative content analysis of AI-enabled digital programmatic advertisements. Although the idea that sponsorship disclosure, appeals, and sentiments are important in persuasive messages has been documented, this study extends existing literature by examining these concepts within AI-enabled programmatic advertising. Sponsorship-linked marketing and congruence theory guided this study. The results showed that only about half of the ads revealed sponsorship information. Further, the brand-sponsor association was not clearly established in the ads. A sentiment analysis showed the prominence of positive sentiments in the ads. Additionally, the ads mostly used product- and consumer-focused appeals. Theoretical and research contributions and strategic marketing implications are discussed. [ABSTRACT FROM AUTHOR]

GaAs thin‐film solar cells have high efficiency, reliability, and operational stability, making them a promising solution for self‐powered skin‐conformal biosensors. However, inherent device thickness limits suitability for such applications, making them uncomfortable and unreliable in flexural environments. Therefore, reducing the flexural rigidity becomes crucial for integration with skin‐compatible electronic devices. Herein, this study demonstrated a novel one‐step surface modification bonding methodology, allowing a streamlined transfer process of ultra‐thin (2.3 µm thick) GaAs solar cells on flexible polymer substrates. This reproducible technique enables strong bonding between dissimilar materials (GaAs‐polydimethylsiloxane, PDMS) without high external pressures and temperatures. The fabricated solar cell showed exceptional performance with an open‐circuit voltage of 1.018 V, short‐circuit current density of 20.641 mA cm−2, fill factor of 79.83%, and power conversion efficiency of 16.77%. To prove the concept, the solar cell is integrated with a skin‐compatible organic electrochemical transistor (OECT). Competitive electrical outputs of GaAs solar cells enabled high current levels of OECT under subtle light intensities lower than 50 mW cm−2, which demonstrates a self‐powered electrocardiogram sensor with low noise (signal‐to‐noise ratio of 32.68 dB). Overall, this study presents a promising solution for the development of free‐form and comfortable device structures that can continuously power wearable devices and biosensors. [ABSTRACT FROM AUTHOR]

On December 31, 2016 amidst New Year's Eve revelry in Bangalore, India, about 700 young women faced a mass molestation. This research analyzed and compared 299 news articles (N=299) between January 1, 2017 and February 1, 2017 from three leading English language newspapers in India through a quantitative content analysis. Results indicated that there were differences in how the regional and national newspapers framed their stories. While national newspapers deployed the episodic frame in their news stories, the local newspaper used both thematic and episodic frames. The episodic frame focuses on individual events, whereas the thematic frame focuses on trends over time. Local newspapers discussed causes and attributions of responsibility more than national newspapers. National newspapers mostly used celebrity and official sources in their coverage, whereas local newspapers covered the testimonies of the victims and eyewitnesses more. Theoretical and practical implications are discussed in this paper. Keywords: Sexual crimes against women, Gender-based violence, Gender and communication, Representation, Framing, Quantitative content analysis, Newspapers narratives, Responsible journalism, The 'Night of Shame' in Bangalore Drawing on a quantitative content analysis of 299 newspaper articles, this research systematically examines the frames in print news coverage of the mass molestation [...]