FQPWZ9

: Predicting student satisfaction on Metaverse platforms can improve educational effectiveness by analyzing factors like engagement, interaction, and content delivery. The benefits include personalized, immersive learning experiences and real-time feedback. However, the existing schemes suffer from overfitting issues due to highly imbalanced data and fail to generalize to diverse content. To address these limitations, a robust model, Deep High Attention Long Short-Term Memory Forward Harmonic Network (DHA-LSTM-FH Net), is proposed to predict student satisfaction in Virtual Reality (VR) teaching within the Metaverse. The model utilizes data from Metaverse platforms, including virtual spaces, Augmented Reality (AR)/VR devices, learning materials, and student information. Initially, interaction logs from VR sessions and student profiles are collected as input. The data undergoes softmax normalization to ensure consistency. Feature selection is conducted using Recursive Feature Elimination (RFE) and Elastic Net to select the key features. Local Densitybased Synthetic Minority Over-Sampling Technique (LD-SMOTE) is then applied to address data imbalance. Student satisfaction prediction is done by DHA-LSTM-FH Net, which is developed by combining Deep High Attention Neural Network (DHA-Net) and Long ShortDownloaded for personal academic use. All rights reserved. https://papernode.online/ Term Memory (LSTM) using Harmonic Analysis. Experimental results show that the model achieves a precision of 93.765%, a recall of 95.755%, an F1 Score of 94.750%, and a Cohen’s Kappa of 0.838, outperforming baseline methods. However, the model is trained on a specific VR/Metaverse platform, so its performance may drop when applied to different Metaverse setups or content types.

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Ischemic stroke severity and mortality after left atrial appendage closure vs nonwarfarin oral anticoagulants in patients with prior stroke

(Abstract not found)

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A secure and efficient identity-based RFID mutual Authentication scheme for IoT using elliptic curve cryptography

Existing ECC-based RFID authentication protocols exhibit vulnerabilities to various attacks. A new ECCbased authentication protocol is proposed to address these issues, ensuring mutual authentication, confidentiality, and resistance to SCA. The proposed protocol minimizes computational time and enhances privacy features without additional calculations. Security and privacy comparisons with existing schemes show the improved protocol’s effectiveness in mitigating threats. The study emphasizes practical implementation and detailed security analysis, highlighting the protocol’s efficiency and security enhancements. Further, it discusses emerging security solutions, including encryption algorithms, secure key exchange protocols, and anomaly detection techniques, t o mitigate potential risks and enhance the overall security posture of IoT and RFID systems. By understanding and addressing these security concerns, organizations and individuals can fully leverage the transformative power of IoT and RFID technologies while safeguarding sensitive data and ensuring the integrity and reliability of interconnected systems. ARTICLE HISTORY Received 1 September 2024 Accepted 4 April 2025 KEYWORDS Internet of things; ECC; RFID; side channel attack; authentication method; AVISPA tool 1. Introduction The Internet of Things (IoT) refers to a connected network of physical objects, devices, and sensors capable of gathering, transmitting, and exchanging data through the Internet. The

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International Journal of Biological Macromolecules

(Abstract not found)

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Computers in Biology and Medicine

(Abstract not found)

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Control Strategies for Real-Time Aerial Manipulation with Multi-DOF Arms: A Survey

This survey summarizes key control approaches and architectures that reflect the state-of-the-art in aerial manipulation. The central objective is to provide a thorough resource for researchers exploring multirotor configurations suitable for real-time aerial manipulation applications. The focus is on evaluating and comparing prototype systems and their corresponding controller designs, emphasizing real-time implementation, regardless of the number of DOFs of the attached manipulator(s) and of specific applications. The survey groups control methods in three categories based on the specific architecture that is followed: coupled, partially coupled, and decoupled. The metrics used for the comparative study include system configuration, total weight, modeling approach, control archite cture, robustness, implementation complexity, task execution precision, and achieved results (via simulations or experiments).

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An exploration of the drivers of employee motivation to facilitate value co-creation

Purpose – This paper aims to explore the drivers of employee motivation to facilitate value co-creation. Specifically, it enhances the understanding of social and contextual elements that contribute towards the co-creation of value. Design/methodology/approach – Embracing an interpretive paradigm, the study draws on 57 in-depth interviews together with participant observation field notes. The data were analysed using thematic analysis. Findings – The findings identify six key drivers that motivate employees to facilitate value co-creation: rewards and recognition, opportunities for life-long learning, interpersonal engagement, role responsibility and accountability, organisational vision and social purpose. Research limitations/implications – This study is undertaken wi thin a traditional organisation setting. Other organisational contexts such as working from home should also be considered. Second, this study focused on the individual relational orientations of employees. Also, there is an opportunity to explore the collective orientation of employees. Originality/value – Drawing on service-dominant logic (S-D logic) as a theoretical lens, this study adopts and adapts Lindenberg and Steg’s (2013) goal-framing theory to conceptualise six drivers of employee motivation to facilitate value co-creation within three-goal frames that leads to in-role and extra-role job performance.

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Technology Affordances, Social Media Engagement, and Social Media Addiction: An Investigation of TikTok, Instagram Reels, and YouTube Shorts

Currently, time spent online viewing short-form video (SFV) has become an increasingly popular activity. SFV users spend over two hours daily across a variety of SFV platforms. Undergirded by the theory of technological affordances, the present study is the first to investigate the relative strength of three tech affordances—recommendation accuracy, serendipity, and perceived effortlessness offered by three popular SFV platforms: TikTok, Instagram Reels, and YouTube Shorts. A survey of 555 college students was conducted. Each respondent was asked to rate each of the three SFV platforms on an 18-item scale that measured the three tech affordances of interest. Respondents then completed scales that measured social media engagement and social media addiction. As po sited, SFV users rated the TikTok platform as offering more tech affordances than Instagram Reels and YouTube Shorts. Study results also found tech affordances derived from TikTok and Instagram Reels indirectly impact addictive social media use through the mediating variable of social media engagement. Study results show affordances offered by SFVs, as designed, are associated with heightened social media engagement, and ultimately, addiction. Future research should investigate these tech affordances and others and their relationship with heightened social media use, as well as how SFV is used (passively or actively) impacts engagement and its potential outcomes. Keywords: short-form video, TikTok, Instagram Reels, Y

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Albumin Nanoparticle‑Based Delivery of Oxaliplatin‑Oleic Acid Prodrug for Enhanced Breast Cancer Therapy

Triple-negative breast cancer (TNBC) remains a therapeutic outlier, with limited targeted options and frequent relapse despite chemotherapy. While platinum therapy can benefit some TNBC cases, including BRCA1/2-mutant tumors, toxicity and limited tumor-selective exposure often restrict its impact. To address these barriers, we applied a lipid-metallodrug prodrug approach and synthesized an oxaliplatin-oleic acid (OXA-OA) conjugate that coupled OXA’s cytotoxicity with OA-associated anticancer activity. The prodrug was encapsulated into genipin-crosslinked albumin nanoparticles (OXA-OA Alb NPs) to improve tumor targeting, yielding a uniform size of 140.52 ± 4.35 nm, a PDI of 0.25 ± 0.05, and an encapsulation efficiency of 84.55 ± 4.49%. Spectrometric analysi s confirmed successful OXA-OA conjugation. The nanoparticles demonstrated enhanced cellular uptake and tumor targeting. In vitro, OXA-OA Alb NPs reduced the ­IC50 to 0.19 ± 0.36 µg/mL (4T1) and 0.20 ± 0.16 µg/mL (MDA-MB-231). This corresponded to 25 to 30-fold higher cytotoxicity than free OXA and > 50-fold than OA. Furthermore, apoptosis indices reached 1.47 (4T1) and 1.42 (MDA-MB-231), which were 4.19- and 4.50-fold higher than OA and 2.43- and 2.53-fold higher than OXA. In vivo, OXA-OA Alb NPs achieved ~ 90% tumor inhibition in a TNBC mouse model, with minimal systemic toxicity, stable liver and kidney function, and reduced organ damage compared with other treatment groups. These findings suggest that OXA-OA

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Ratiometric Biomimetic Sensor Based on Quantum Dots − Enhanced Glycosylated Carbon Dots for Visual Detection of Escherichia coli

: We report a fluorescence biomimetic sensor that integrates carbon dots (CDs) and CdTe quantum dots (QDs) for the rapid, antibody-free, and aptamer-free detection of Escherichia coli O157:H7 (E. coli O157:H7) in water samples. This biosensor operates on a ratiometric principle, leveraging green-emitting CDs (GCDs) as a dynamic signal with the red-emitting QDs (RQDs) as a stable reference that significantly enhances the sensor's sensitivity, enabling straightforward visual detection of bacterial contamination. Selectivity was achieved by conjugating mannose
a natural biomimetic receptor
to GCDs, allowing specific recognition of FimH proteins on E. coli O157:H7. Upon bacterial binding, the green emission at 508 nm intensifies proportionally to bacterial concentra tion, while the red fluorescence at 694 nm remains unchanged. This ratiometric biomimetic sensor detects bacterial concentrations ranging from 101 to 108 CFU/mL within 30 min. The biosensor provides a simple, on-site, naked-eye detection under a portable blue-LED light, with color changes indicating water safety status: green for high bacterial loads, yellow/ orange for moderate contamination, and red for safe water. It was successfully applied to tap water, bottled mineral water, surface and groundwater, as well as to samples collected at multiple stages of water treatment plants (12 sources; five replicates). Comprehensive characterization of the nanomaterials was performed using Fourier-transform infrared spectrosc

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