Search Results (1,573)

This study reports the development of electrospun poly(methyl methacrylate) (PMMA) fiber mats incorporating ibuprofen (IBU)-intercalated layered double hydroxides (LDH) for enhanced transdermal drug delivery systems (TDDS). IBU, in its anionic form, was successfully intercalated into LDH, which possesses anion exchange capabilities, and subsequently embedded into PMMA fibers via electrospinning. In vitro drug release experiments demonstrated that UPMMA–LDH–IBU fibers exhibited significantly higher IBU release than PMMA–IBU controls. This enhancement was attributed to the improved hydrophilicity and water absorption imparted by the LDH, as confirmed by contact angle and water uptake measurements. Furthermore, artificial skin permeation tests revealed that the UPMMA–LDH–IBU fibers maintained comparable release rates to those observed during buffer immersion, indicating that the rate-limiting step was the diffusion of IBU within the fiber matrix rather than the interface with the skin or buffer. These findings highlight the critical role of LDH in modulating drug release behavior and suggest that UPMMA–LDH–IBU electrospun fiber mats offer a promising and efficient platform for advanced TDDS applications. Full article

Short-form video platforms have shifted advertising from standalone, time-bounded spots to feed-embedded, swipeable stimuli, creating a high-velocity processing context that can penalize casting complexity. We ask whether a “one face, many roles” casting strategy (a single actor playing multiple characters) outperforms multi-actor executions, and why. A two-phase pretest (N = 3500) calibrated a realistic ceiling for “multi-actor” casts, then four experiments (total N = 4513) tested mechanisms, boundary conditions, and alternatives. Study 1 (online and offline replications) shows that single-actor ads lower cognitive load and boost account evaluations and purchase intention. Study 2, a field experiment, demonstrates that Need for Closure amplifies these gains via reduced cognitive load. Study 3 documents brand-type congruence: one actor performs better for entertaining/exciting brands, whereas multi-actor suits professional/competence-oriented brands. Study 4 rules out cost-frugality and sympathy using a budget cue and a sequential alternative path (perceived cost constraint → sympathy). Across studies, a chain mediation holds: single-actor casting reduces cognitive load, which elevates brand authenticity and increases purchase intention; a simple mediation links cognitive load to account evaluations. Effects are robust across settings and participant gender. We theorize short-form advertising as a context-embedded persuasion episode that connects information-processing efficiency to authenticity inferences, and we derive practical guidance for talent selection and script design in short-form campaigns. Full article

The rise of social media platforms has fundamentally reshaped the global information ecosystem, fostering the spread of disinformation. Beyond the circulation of false content, this article frames disinformation as an aesthetic crisis of public communication: an algorithmic reorganization of sensory experience that privileges performative virality over shared intelligibility, fragmenting public discourse and undermining democratic deliberation. Drawing on John Dewey’s philosophy of aesthetic experience and critical theory (Adorno, Benjamin, Fuchs, Han), we argue that journalism, understood as a form of public art rather than mere fact-transmission, can counteract this crisis by cultivating critical attention, narrative depth, and democratic engagement. We introduce the concept of aesthetic literacy as an extension of media literacy, equipping citizens to discern between seductive but superficial forms and genuinely transformative experiences. Empirical examples from Portugal (Expresso, Público, Mensagem de Lisboa) illustrate how multimodal journalism—through paced narratives, interactivity, and community dialogue—can reconstruct Deweyan “integrated experience” and resist algorithmic disinformation. We propose three axes of intervention: (1) public education oriented to aesthetic sensibility; (2) journalistic practices prioritizing ambiguity and depth; and (3) algorithmic transparency. Defending journalism as a public art of experience is thus crucial for democratic regeneration in the era of sensory capitalism, offering a framework to address the structural inequalities embedded in global information flows. Full article

Impulsive purchasing behavior among university students has gained increased attention in the context of digital consumption settings; however, much of the existing research is product-specific and quantitative, leaving the subjective nuances of this phenomenon underexplored. This study investigates how college students perceive and explain their impulsive purchase behavior across various product categories and platforms, using qualitative data from focus groups (n = 72). By revealing the prevalence of key patterns—interest-aligned, emotional relief, hedonistic lifestyle, social influence, inquisitive reviewer, presentation appeal, and controlled purchase—this research uncovers the underlying identity-affirming practices, internal emotional negotiations, and external sociotechnical cues that shape such behavior. Ultimately, it reframes impulsive buying as a socially embedded, identity-driven act rather than an act of irrationality. These findings advance our understanding of consumer psychology by emphasizing the lived experiences and self-construction processes of young consumers navigating media-saturated, algorithmically curated purchasing environments. Full article

Diffuse gliomas are the most common primary brain tumors in adults in the Western world. According to the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors, the assessment of isocitrate dehydrogenase (IDH1/2)-mutation status is essential for accurate patient stratification. In this study, we performed a comprehensive evaluation of IDH-mutation status in the intraoperative setting using the Idylla platform. The reference cohort comprised 30 formalin-fixed paraffin-embedded (FFPE) tissue samples with known IDH status, while the exploration cohort included 35 intraoperative snap-frozen and native-tissue specimens. The results were compared with those of a standard next-generation sequencing (NGS) analysis. Our findings demonstrate that the Idylla IDH-mutation assay provides 100% concordance compared with NGS analysis for both FFPE and intraoperative tissue samples. The Idylla system delivers results within approximately 90 min, significantly outperforming NGS, which requires between 7 and 27 days. This rapid turnaround facilitates timely interdisciplinary case discussions and enables timely therapy planning, within the framework of neuro-oncological molecular tumor boards. The ultra-fast intraoperative IDH-mutation analysis using the Idylla platform, in combination with intraoperative histopathological assessment, enables rapid patient stratification and treatment planning in diffuse gliomas. Full article

Microcontroller units (MCUs) serve as the core components of embedded systems. In the era of smart IoT, embedded devices are increasingly deployed on mobile platforms, leading to a growing demand for low-power consumption. As a result, low-power technology for MCUs has become increasingly critical. This paper systematically reviews the development history and current technical challenges of MCU low-power technology. It then focuses on analyzing system-level low-power optimization pathways for integrating MCUs with artificial intelligence (AI) technology, including lightweight AI algorithm design, model pruning, AI acceleration hardware (NPU, GPU), and heterogeneous computing architectures. It further elaborates on how AI technology empowers MCUs to achieve comprehensive low power consumption from four dimensions: task scheduling, power management, inference engine optimization, and communication and data processing. Through practical application cases in multiple fields such as smart home, healthcare, industrial automation, and smart agriculture, it verifies the significant advantages of MCUs combined with AI in performance improvement and power consumption optimization. Finally, this paper focuses on the key challenges that still need to be addressed in the intelligent upgrade of future MCU low power consumption and proposes in-depth research directions in areas such as the balance between lightweight model accuracy and robustness, the consistency and stability of edge-side collaborative computing, and the reliability and power consumption control of the sensor-storage-computing integrated architecture, providing clear guidance and prospects for future research. Full article

This paper presents a novel mechanically beam-steered antenna system for 10 GHz applications, enabled by multi-material 3D-printing technology. The proposed design eliminates the need for complex electronic circuitry by integrating a mechanically rotatable, 3D-printed hemispherical lens with a conventional rectangular patch antenna. The system comprises three main components: a 10-GHz patch antenna, a precision-fabricated hemispherical dielectric lens produced via stereolithography (SLA), and a structurally robust rotation assembly fabricated using fused deposition modeling (FDM). The mechanical rotation of the lens enables discrete beam-steering from −45° to +45° in 5° steps. Experimental results demonstrate a gain improvement from 6.21 dBi (standalone patch) to 10.47 dBi with the integrated lens, with minimal degradation across steering angles (down to 9.59 dBi). Simulations and measurements show strong agreement, with the complete system achieving 94% accuracy in beam direction. This work confirms the feasibility of integrating additive manufacturing with passive beam-steering structures to deliver a low-cost, scalable, and high-performance alternative to electronically scanned arrays. Moreover, the design is readily adaptable for motorized actuation and closed-loop control via embedded systems, enabling future development of real-time, programmable beam-steering platforms. Full article

Early wildfire detection in remote areas remains a critical challenge due to limited connectivity, intermittent solar energy, and the need for autonomous, long-term operation. Existing systems often rely on fixed sensing schedules or cloud connectivity, making them impractical for energy-constrained deployments. We introduce EcoWild, a reinforcement learning-driven cyber-physical system for energy-adaptive wildfire detection on solar-powered edge devices. EcoWild combines a decision tree-based fire risk estimator, lightweight on-device smoke detection, and a reinforcement learning agent that dynamically adjusts sensing and communication strategies based on battery levels, solar input, and estimated fire risk. The system models realistic solar harvesting, battery dynamics, and communication costs to ensure sustainable operation on embedded platforms. We evaluate EcoWild using real-world solar, weather, and fire image datasets in a high-fidelity simulation environment. Results show that EcoWild consistently maintains responsiveness while avoiding battery depletion under diverse conditions. Compared to static baselines, it achieves 2.4× to 7.7× faster detection, maintains moderate energy consumption, and avoids system failure due to battery depletion across 125 deployment scenarios. Full article

Aiming at the problems of traditional calibrated visual servo systems relying on precise model calibration and the high training cost and low efficiency of online reinforcement learning, this paper proposes a Multi-Network Mean Delayed Deep Deterministic Policy Gradient Algorithm with Behavior Cloning (MN-MD3+BC) for uncalibrated visual adaptive control of robotic arms. The algorithm improves upon the Twin Delayed Deep Deterministic Policy Gradient (TD3) network framework by adopting an architecture with one actor network and three critic networks, along with corresponding target networks. By constructing a multi-critic network integration mechanism, the mean output of the networks is used as the final Q-value estimate, effectively reducing the estimation bias of a single critic network. Meanwhile, a behavior cloning regularization term is introduced to address the common distribution shift problem in offline reinforcement learning. Furthermore, to obtain a high-quality dataset, an innovative data recombination-driven dataset creation method is proposed, which reduces training costs and avoids the risks of real-world exploration. The trained policy network is embedded into the actual system as an adaptive controller, driving the robotic arm to gradually approach the target position through closed-loop control. The algorithm is applied to uncalibrated multi-degree-of-freedom robotic arm visual servo tasks, providing an adaptive and low-dependency solution for dynamic and complex scenarios. MATLAB simulations and experiments on the WPR1 platform demonstrate that, compared to traditional Jacobian matrix-based model-free methods, the proposed approach exhibits advantages in tracking accuracy, error convergence speed, and system stability. Full article

This paper presents a novel hardware architecture for implementing real-time EMG feature extraction and dimensionality reduction in resource-constrained FPGA environments. The proposed co-processing architecture integrates four time-domain feature extractors (MAV, WL, SSC, ZC) with a specialized PCA matrix multiplication unit within a unified processing pipeline, demonstrating significant improvements in power efficiency and processing latency compared to traditional software-based approaches. Multiple matrix multiplication architectures are evaluated to optimize FPGA resource utilization while maintaining deterministic real-time performance using a Zed evaluation board as the development platform. This implementation achieves efficient dimensionality reduction with minimal hardware resources, making it suitable for embedded prosthetic applications. The functionality of this system is validated using a custom EMG database from previous studies. The results demonstrate a 7.3× speed improvement and 3.1× energy efficiency gain compared to ARM Cortex-A9 software implementation, validating the architectural approach for battery-powered prosthetic control applications. Full article

Training users to correctly identify potential security threats like social engineering attacks such as phishing emails is a crucial aspect of cybersecurity. One challenge in this training is providing useful educational feedback to maximize student learning outcomes. Large Language Models (LLMs) have recently been applied to wider and wider applications, including domain-specific education and training. These applications of LLMs have many benefits, such as cost and ease of access, but there are important potential biases and constraints within LLMs. These may make LLMs worse teachers for important and vulnerable subpopulations including the elderly and those with less technical knowledge. In this work we present a dataset of LLM embeddings of conversations between human students and LLM teachers in an anti-phishing setting. We apply these embeddings onto an analysis of human–LLM educational conversations to develop specific and actionable targets for LLM training, fine-tuning, and evaluation that can potentially improve the educational quality of LLM teachers and ameliorate potential biases that may disproportionally impact specific subpopulations. Specifically, we suggest that LLM teaching platforms either speak generally or mention specific quotations of emails depending on user demographics and behaviors, and to steer conversations away from an over focus on the current example. Full article

The future of anxiety management lies in bridging traditional evidence-based treatments with intelligent and adaptive digital platforms. Embedding multi-agent systems capable of real-time mood detection and self-management support represents a transformative step towards intelligent care, enabling users to independently regulate acute episodes, prevent relapse, and promote sustained personal well-being. These digital solutions illustrate how technology can improve accessibility, personalization, and adherence, while establishing the foundation for integrating multi-agent architectures into mental health systems. Such architectures can continuously detect and interpret users’ emotional states through multimodal data, coordinating specialized agents for monitoring, personalization, and intervention. Crucially, they extend beyond passive data collection to provide active, autonomous support during moments of heightened anxiety, guiding individuals through non-pharmacological strategies such as breathing retraining, grounding techniques, or mindfulness practices without requiring immediate professional involvement. By operating in real time, multi-agent systems function as intelligent digital companions capable of anticipating needs, adapting to context, and ensuring that effective coping mechanisms are accessible at critical moments. This paper presents a multi-agent architecture for the digital management of anxiety episodes, designed not only to enhance everyday well-being but also to deliver immediate, personalized assistance during unexpected crises, offering a scalable pathway towards intelligent, patient-centered mental health care. Full article

In fresh produce cold chain last-mile delivery, the highly dispersed customer base leads to exorbitant delivery costs, posing the greatest challenge for cold chain enterprises. Achieving a symmetrical balance between cost-efficiency, environmental sustainability, and service quality is a fundamental pursuit in logistics system optimization. This paper proposes integrating the crowd-shipping logistics model—characterized by internet platform sharing and flexibility—into the delivery service. It incorporates and extends features such as cold chain delivery, mixed fleets using gasoline and diesel vehicles (GDVs), electric vehicles (EVs), partial charging strategies for EVs, and time-of-use electricity pricing into the crowd-shipping model. A joint delivery mode combining traditional professional delivery (using GDVs and EVs) with crowd-shipping is proposed, creating a symmetrical collaboration between centralized fleet management and distributed social resources. The challenges associated with utilizing occasional drivers (ODs) are analyzed, along with the corresponding compensation decisions and allocation-related constraints. A route optimization model is constructed with the objective of minimizing total cost. To solve this model, an Improved Whale Optimization Algorithm (IWOA) is proposed. To further enhance the algorithm’s performance, an adaptive variable neighborhood search is embedded within the proposed algorithm, and four local search operators are applied. Using a case study of 100 customer nodes, the joint delivery mode with OD participation reduces total delivery costs by an average of 24.94% compared to the traditional professional vehicle delivery mode, demonstrating a more symmetrical allocation of logistical resources. The experiments fully demonstrate the effectiveness of the joint delivery model and the proposed algorithm. Full article

This work presents a low-power photoplethysmography (PPG) readout integrated circuit (IC) that achieves a wide dynamic range (DR) through the direct integration of a voltage-controlled oscillator (VCO)-based quantizer into the photodiode driver. Conventional PPG readout circuits rely on either transimpedance amplifier (TIA) or light-to-digital converter (LDC) topologies, both of which require auxiliary DC suppression loops. These additional loops not only raise power consumption but also limit the achievable DR. The proposed design eliminates the need for such circuits by embedding a linear regulator with a mirroring scale calibrator and a time-domain quantizer. The quantizer provides first-order noise shaping, enabling accurate extraction of the AC PPG signal while the regulator directly handles the large DC current component. Post-layout simulations show that the proposed readout achieves a signal-to-noise-and-distortion ratio (SNDR) of 40.0 dB at 10 µA DC current while consuming only 0.80 µW from a 2.5 V supply. The circuit demonstrates excellent stability across process–voltage–temperature (PVT) corners and maintains high accuracy over a wide DC current range. These features, combined with a compact silicon area of 0.725 mm² using TSMC 250 nm bipolar–CMOS–DMOS (BCD) process, make the proposed IC an attractive candidate for next-generation wearable and biomedical sensing platforms. Full article

Background: Transitions from hospital to long-term care (LTC) facilities are critical periods for older adults living with dementia, often involving complex medical, cognitive, and psychosocial needs. Virtual team-based care has emerged as a promising strategy to improve communication, coordination, and continuity of care during these transitions. However, there is limited evidence on how such approaches are implemented in practice, particularly with respect to inclusion, equity, and engagement of older adults and families. Objective: This study aimed to identify the enablers and barriers to delivering virtual team-based care to support older adults with dementia in transitioning from hospital to LTC. Methods: We conducted a qualitative study using semi-structured interviews, focus groups, and a policy review. Data were collected from 60 participants, including healthcare providers, older adults, and family care partners across hospital and LTC settings in British Columbia, Canada. Thematic analysis was conducted using a hybrid inductive and deductive approach. Eighteen institutional policies and guidelines on virtual care and dementia transitions were reviewed to contextualize findings. Results: Four themes were identified: (1) enhancing communication and collaboration, (2) engaging families in care planning, (3) digital access and literacy, and (4) organizational readiness and infrastructure. While virtual huddles and secure messaging platforms supported timely coordination, implementation was inconsistent due to infrastructure limitations, unclear protocols, and staffing pressures. Institutional policies emphasized privacy and security but lacked guidance for inclusive engagement of older adults and families. Many participants described limited access to reliable technology, a lack of training, and the absence of tools tailored for individuals with cognitive impairment. Conclusions: Virtual care has the potential to support more coordinated and inclusive transitions for people with dementia, but its success depends on more than technology. Structured protocols, inclusive policies, and leadership commitment are essential to ensure equitable access and meaningful engagement. The proposed VIRTUAL framework offers practical tips for strengthening virtual team-based care by embedding ethical, relational, and infrastructural readiness across settings. Full article