Search Results (4,472)

Natural-fiber-reinforced polypropylene (PP) composites are gaining increasing interest as lightweight, sustainable alternatives for various packaging and applications. This study investigates the effect of filler addition sequence on the mechanical, morphological, thermal, and dynamic mechanical properties of PP-based composites reinforced with graphite nanoplatelets (GnP) and kenaf fiber (KF). Two filler incorporation sequences were evaluated: GnP/KF/PP (GnP initially mixed with KF before PP addition) and GnP/PP/KF (KF added after mixing GnP with PP). The GnP/KF/PP composite exhibited superior mechanical properties, with tensile strength and flexural strength increasing by up to 25% compared to the control, while GnP/PP/KF showed a 13% improvement. SEM analyses revealed that initial mixing of GnP with KF significantly improved filler dispersion and interfacial bonding, enhancing stress transfer within the composite. XRD and DSC analyses showed reduced crystallinity and lower crystallization temperatures in the addition of KF due to restricted polymer chain mobility. Thermal stability assessed by TGA indicated minimal differences between the composites regardless of filler sequence. DMA results demonstrated a significantly higher storage modulus and enhanced elastic response in the addition of KF, alongside a slight decrease in glass transition temperature (T_g). The results emphasize the importance of optimizing filler addition sequences to enhance mechanical performance, confirming the potential of these composites in sustainable packaging and structural automotive applications. Full article

This paper presents a novel framework for optimizing multimodal large language model (MLLM) inference through task offloading and resource allocation in UAV-assisted satellite edge computing (SEC) networks. MLLMs leverage transformer architectures to integrate heterogeneous data modalities for IoT applications, particularly real-time monitoring in remote areas. However, cloud computing dependency introduces latency, bandwidth, and privacy challenges, while IoT device limitations require efficient distributed computing solutions. SEC, utilizing low-earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs), extends mobile edge computing to provide ubiquitous computational resources for remote IoTDs. We formulate the joint optimization of MLLM task offloading and resource allocation as a mixed-integer nonlinear programming (MINLP) problem, minimizing latency and energy consumption while optimizing offloading decisions, power allocation, and UAV trajectories. To address the dynamic SEC environment characterized by satellite mobility, we propose an action-decoupled soft actor–critic (AD-SAC) algorithm with discrete–continuous hybrid action spaces. The simulation results demonstrate that our approach significantly outperforms conventional deep reinforcement learning methods in convergence and system cost reduction compared to baseline algorithms. Full article

Gallium nitride high-electron-mobility transistors (GaN HEMTs) are critical for high-power applications like AI power supplies and robotics but face reliability challenges due to increased dynamic ON-resistance (R_{DS_ON}) from electrical and thermomechanical stresses. This paper presents a novel self-calibrating temperature-sensitive electrical parameter (TSEP) model that uses gate leakage current (I_G) to estimate junction temperature with high accuracy, uniquely addressing aging effects overlooked in prior studies. By integrating I_G, aging-induced degradation, and failure-in-time (FIT) models, the approach achieves a junction temperature estimation error of less than 1%. Long-term hard-switching tests confirm its effectiveness, with calibrated R_{DS_ON} measurements enabling precise remaining useful life (RUL) predictions. This methodology significantly improves GaN HEMT reliability assessment, enhancing their performance in resilient power electronics systems. Full article

Uniquely rooted in regenerative leadership and complemented by Complexity Leadership Theory (CLT), this conceptual essay offers a theoretical exploration of how educational institutions can act as dynamic systems that catalyze adaptive, community-led responses to anthropocentric socio-environmental crises. Rather than sustaining existing structures, educational leadership for regeneration seeks to restore ecological balance and nurture emergent capacities for long-term resilience. Positioned as key sites of influence, educational institutions are explored as engines of innovation capable of mobilizing students, educators, and communities toward collective environmental action. CLT offers a valuable lens for understanding how leadership emerges from nonlinear, adaptive processes within schools, enabling the development of innovative, collaborative, and responsive strategies required for navigating complexity and leading planetary-positive change. Drawing on a synthesis of the recent global literature, this paper begins by outlining the need to go beyond sustainability in envisioning regenerative futures, followed by an introduction to regenerative principles. It then examines the current and evolving role of educational leadership, the relevance in enabling whole-institution transformation, and how this relates to regenerative practices. The theoretical frameworks of systems thinking and CLT are introduced before noting their application within regenerative educational leadership. The final sections identify implementation challenges and offer practical recommendations, including curriculum innovation, professional development, and youth-led advocacy, before concluding with a call for education as a vehicle for cultivating planetary-conscious citizens and systemic change. This work contributes a timely and theoretically grounded model for reimagining educational leadership in an era of global turbulence. Full article

Urban road traffic congestion poses significant challenges to sustainable mobility in smart cities. Traditional traffic light systems, reliant on static or semi-fixed timers, fail to adapt to dynamic traffic conditions, exacerbating congestion and limiting inclusivity. To address these limitations, this paper proposes H-ATLM (a hybrid adaptive traffic lights management), a system utilizing the deep deterministic policy gradient (DDPG) reinforcement learning algorithm to optimize traffic light timings dynamically based on real-time data. The system integrates advanced sensing technologies, such as cameras and inductive loops, to monitor traffic conditions and adaptively adjust signal phases. Experimental results demonstrate significant improvements, including reductions in congestion (up to 50%), increases in throughput (up to 149%), and decreases in clearance times (up to 84%). These findings open the door for integrating accessibility-focused features such as adaptive signaling for accessible vehicles, dedicated lanes for paratransit services, and prioritized traffic flows for inclusive mobility. Full article

Ubiquitous blended learning, leveraging mobile devices, has democratized education by enabling autonomous and readily accessible knowledge acquisition. However, its reliance on traditional interfaces often limits learner immersion and meaningful interaction. The emergence of the wearable metaverse offers a compelling solution, promising enhanced multisensory experiences and adaptable learning environments that transcend the constraints of conventional ubiquitous learning. This research proposes a novel framework for ubiquitous blended learning in the wearable metaverse, aiming to address critical challenges, such as multi-source data fusion, effective human–computer collaboration, and efficient rendering on resource-constrained wearable devices, through the integration of embodied interaction and multi-agent collaboration. This framework leverages a real-time multi-modal data analysis architecture, powered by the MobileNetV4 and xLSTM neural networks, to facilitate the dynamic understanding of the learner’s context and environment. Furthermore, we introduced a multi-agent interaction model, utilizing CrewAI and spatio-temporal graph neural networks, to orchestrate collaborative learning experiences and provide personalized guidance. Finally, we incorporated lightweight SLAM algorithms, augmented using visual perception techniques, to enable accurate spatial awareness and seamless navigation within the metaverse environment. This innovative framework aims to create immersive, scalable, and cost-effective learning spaces within the wearable metaverse. Full article

This paper examines how Marley Dias’ activism on Instagram promotes racial justice, Black feminist thought, and youth mobilization through digital storytelling, representation, and audience engagement. Using a mixed-methods analysis of 744 posts published between 2016 and 2025, the study combined critical thematic coding, temporal mapping, and engagement metrics to analyze the discursive and emotional strategies behind Dias’ activism. Five key themes were identified as central to her activist work: diversity in literature, lack girl empowerment, racial justice, Black representation, and educational advocacy. The findings reveal that Dias strategically tailors her messages to suit Instagram’s unique features, using carousels and videos to enhance visibility, foster intimacy, and provide depth in education. Posts that focused on identity, aesthetics, and empowerment garnered the highest levels of engagement, while posts that concentrated on structural issues received lower, yet still significant, interaction. The paper argues that Dias’ Instagram account serves as a dynamic platform for youth-led Black feminist resistance, where cultural production, civic education, and emotional impact converge. This case underscores the political potential of digital literacies and encourages a reconsideration of how youth-driven digital activism is reshaping contemporary public discourse, agency, and knowledge production in the social media age. Full article

With the widespread adoption of mobile reading across diverse scenarios, understanding environmental impacts on attention has become crucial for reading performance optimization. Building upon this premise, the study examined the impacts of different reading environments on attention during mobile reading, utilizing a mixed-methods approach that combined eye-tracking experiments with semi-structured interviews. Thirty-two college students participated in the study. Quantitative attention metrics, including total fixation duration and fixation count, were collected through eye-tracking, while qualitative data regarding perceived environmental influences were obtained through interviews. The results indicated that the impact of different environments on mobile reading attention varies significantly, as this variation is primarily attributable to environmental complexity and individual interest. Environments characterized by multisensory inputs or dynamic disturbances, such as fluctuating noise and visual motion, were found to induce greater attentional dispersion compared to monotonous, low-variation environments. Notably, more complex potential task-like disturbances (e.g., answering calls, conversations) were found to cause the greatest distraction. Moreover, stimuli aligned with an individual’s interests were more likely to divert attention compared to those that did not. These findings contribute methodological insights for optimizing mobile reading experiences across diverse environmental contexts. Full article

Motor Imagery (MI) based Brain Computer Interfaces (BCIs) have promising applications in neurorehabilitation for individuals who have lost mobility and control over parts of their body due to brain injuries, such as stroke patients. Accurately classifying MI tasks is essential for effective BCI performance, but this task remains challenging due to the complex and non-stationary nature of EEG signals. This study aims to improve the classification of left and right-hand MI tasks by utilizing high-resolution time-frequency features extracted from EEG signals, enhanced with deep learning-based data augmentation techniques. We propose a novel deep learning framework named the Generalized Wavelet Transform-based Deep Convolutional Network (GDC-Net), which integrates multiple components. First, EEG signals recorded from the C3, C4, and Cz channels are transformed into detailed time-frequency representations using the Generalized Morse Wavelet Transform (GMWT). The selected features are then expanded using a Deep Convolutional Generative Adversarial Network (DCGAN) to generate additional synthetic data and address data scarcity. Finally, the augmented feature maps data are subsequently fed into a hybrid CNN-LSTM architecture, enabling both spatial and temporal feature learning for improved classification. The proposed approach is evaluated on the BCI Competition IV dataset 2b. Experimental results showed that the mean classification accuracy and Kappa value are 89.24% and 0.784, respectively, making them the highest compared to the state-of-the-art algorithms. The integration of GMWT and DCGAN significantly enhances feature quality and model generalization, thereby improving classification performance. These findings demonstrate that GDC-Net delivers superior MI classification performance by effectively capturing high-resolution time-frequency dynamics and enhancing data diversity. This approach holds strong potential for advancing MI-based BCI applications, especially in assistive and rehabilitation technologies. Full article

Ultraviolet (UV) communication, characterized by non-line-of-sight (NLOS) scattering, holds substantial potential for enabling communication networking in unmanned aerial vehicle (UAV) formations within strong electromagnetic interference environments. This paper proposes a networking protocol for large-scale mobile ultraviolet communication networks (LSM-UVCN). In large-scale networks, the proposed protocol establishes multiple non-interfering transmission paths based on a connection matrix simultaneously, ensuring reliable space division multiplexing (SDM) and optimizing the utilization of network channel resources. To address frequent network topology changes in mobile scenarios, the protocol employs periodic maintenance of the connection matrix, significantly reducing the adverse impacts of node mobility on network performance. Simulation results demonstrate that the proposed protocol achieves superior performance in large-scale mobile UV communication networks. By dynamically adjusting the connection matrix update frequency, it adapts to varying node mobility intensities, effectively minimizing control overhead and data loss rates while enhancing network throughput. This work underscores the protocol’s adaptability to dynamic network environments, providing a robust solution for high-reliability communication requirements in complex electromagnetic scenarios, particularly for UAV swarm applications. The integration of SDM and adaptive matrix maintenance highlights its scalability and efficiency, positioning it as a viable technology for next-generation wireless communication systems in challenging operational conditions. Full article

Daoist robes in the Ming Dynasty literature underwent a marked transformation from exclusive religious vestments to widespread secular attire. Originally confined to Daoist priests and sacred rites, these garments began to appear in everyday work, entertainment, and ceremonies across social strata. Drawing on a hand-coded corpus of novels that yields robe related passages, and by analyzing textual references from Ming novels, Daoist canonical works, and visual artifacts, and applying clothing psychology and semiotic theory, this study elucidates how Daoist robes were re-coded as secular fashion symbols. For example, scholar-officials donned Daoist robes to convey moral prestige, laborers adopted them to signal upward mobility, and merchants donned them to impersonate the educated elite for commercial gain. By integrating close textual reading with cultural theory, the article advances a three-stage model, sacred uniform, ritual costume, and secular fashion, that clarifies the semantic flow of Daoist robes. In weddings and funerals, many commoners flaunted Daoist robes despite sumptuary laws, using them to assert honor and status. These adaptations reflect both the erosion of Daoist institutional authority and the dynamic process of identity construction through dress in late Ming society. Our interdisciplinary analysis highlights an East Asian perspective on the interaction of religion and fashion, offering historical insight into the interplay between religious symbolism and sociocultural identity formation. Full article

Urban traffic congestion poses persistent challenges to mobility, public safety, and governance efficiency in metropolitan areas. This study proposes an intelligent traffic flow forecasting framework based on an extended Long Short-Term Memory (xLSTM) model, specifically designed for real-time congestion prediction and proactive police dispatch support. Utilizing a real-world dataset collected from over 300 vehicle detector (VD) sensors, the proposed model integrates vehicle volume, speed, and lane occupancy data at five-minute intervals. Methodologically, the xLSTM model incorporates matrix-based memory cells and exponential gating mechanisms to enhance spatio-temporal learning capabilities. Model performance is evaluated using multiple metrics, including congestion classification accuracy, F1-score, MAE, RMSE, and inference latency. The xLSTM model achieves a congestion prediction accuracy of 87.3%, an F1-score of 0.882, and an average inference latency of 41.2 milliseconds—outperforming baseline LSTM, GRU, and Transformer-based models in both accuracy and speed. These results validate the system’s suitability for real-time deployment in police control centers, where timely prediction of traffic congestion enables anticipatory patrol allocation and dynamic signal adjustment. By bridging AI-driven forecasting with public safety operations, this research contributes a validated and scalable approach to intelligent transportation governance, enhancing the responsiveness of urban mobility systems and advancing smart city initiatives. Full article

Accurate and rapid aquatic organism recognition is a core technology for fisheries automation and aquatic organism statistical research. However, due to absorption and scattering effects, images of aquatic organisms often suffer from poor contrast and color distortion. Additionally, the clustering behavior of aquatic organisms often leads to occlusion, further complicating the identification task. This study proposes a lightweight object detection model, Mobile-YOLO, for the recognition of four representative aquatic organisms, namely holothurian, echinus, scallop, and starfish. Our model first utilizes the Mobile-Nano backbone network we proposed, which enhances feature perception while maintaining a lightweight design. Then, we propose a lightweight detection head, LDtect, which achieves a balance between lightweight structure and high accuracy. Additionally, we introduce Dysample (dynamic sampling) and HWD (Haar wavelet downsampling) modules, aiming to optimize the feature fusion structure and achieve lightweight goals by improving the processes of upsampling and downsampling. These modules also help compensate for the accuracy loss caused by the lightweight design of LDtect. Compared to the baseline model, our model reduces Params (parameters) by 32.2%, FLOPs (floating point operations) by 28.4%, and weights (model storage size) by 30.8%, while improving FPS (frames per second) by 95.2%. The improvement in mAP (mean average precision) can also lead to better accuracy in practical applications, such as marine species monitoring, conservation efforts, and biodiversity assessment. Furthermore, the model’s accuracy is enhanced, with the mAP increased by 1.6%, demonstrating the advanced nature of our approach. Compared with YOLO (You Only Look Once) series (YOLOv5-12), SSD (Single Shot MultiBox Detector), EfficientDet (Efficient Detection), RetinaNet, and RT-DETR (Real-Time Detection Transformer), our model achieves leading comprehensive performance in terms of both accuracy and lightweight design. The results indicate that our research provides technological support for precise and rapid aquatic organism recognition. Full article

This study investigates the application of the Dueling Double Deep Q-Network (3DQN) algorithm to optimize traffic signal control at a major urban intersection in Danang City, Vietnam. The objective is to enhance signal timing efficiency in response to mixed traffic flow and real-world traffic dynamics. A simulation environment was developed using the Simulation of Urban Mobility (SUMO) software version 1.11, incorporating both a fixed-time signal controller and two 3DQN models trained with 1 million (1M-Step) and 5 million (5M-Step) iterations. The models were evaluated using randomized traffic demand scenarios ranging from 50% to 150% of baseline traffic volumes. The results demonstrate that the 3DQN models outperform the fixed-time controller, significantly reducing vehicle delays, with the 5M-Step model achieving average waiting times of under five minutes. To further assess the model’s responsiveness to real-time conditions, traffic flow data were collected using YOLOv8 for object detection and SORT for vehicle tracking from live camera feeds, and integrated into the SUMO-3DQN simulation. The findings highlight the robustness and adaptability of the 3DQN approach, particularly under peak traffic conditions, underscoring its potential for deployment in intelligent urban traffic management systems. Full article

As data becomes a core driver of modern business innovation, mobile applications increasingly collect and process users’ personal information, posing significant challenges to the effectiveness of informed consent and the legitimacy of user authorization. Existing research on privacy informed consent mechanisms has predominantly focused on privacy policy texts and normative legal discussions, often overlooking a critical touchpoint—the launch-time privacy pop-up window. Moreover, empirical investigations from the user’s perspective remain limited. To address these issues, this study employs a two-stage approach combining compliance audit and grounded theory. The preliminary audit of 21 mobile apps assesses the compliance of privacy pop-ups, and the formal study uses thematic analysis of interviews with 19 participants to construct a dual-path explanatory framework. Key findings reveal that: (1) while the reviewed apps partially safeguarded users’ right to be informed, compliance deficiencies still persist; (2) trust and privacy fatigue emerge as dual motivations driving user consent. Trust plays a critical role in amplifying the impact of positive messages within privacy pop-ups by enhancing the consistency among users’ cognition, affect, and behavior, thereby reducing resistance to privacy consent and improving the effectiveness of the current informed consent framework. Conversely, privacy fatigue increases the inconsistency among these factors, undermining consent effectiveness and exacerbating the challenges associated with informed consent. This study offers a user-centered framework to explain the dynamics of informed consent in mobile privacy pop-ups and provides actionable insights for regulators, developers, and privacy advocates seeking to enhance transparency and user autonomy. Full article