Search Results (1,408)

Usnic acid (UA), a prominent lichen secondary metabolite, exhibits a unique dual therapeutic profile in dermatology, though its clinical translation is limited by systemic hepatotoxicity and poor solubility. This review comprehensively evaluates the topical efficacy, molecular mechanisms, and advanced formulation strategies of UA enantiomers and UA-rich extracts. A literature search across PubMed, Scopus, and Google Scholar identified 36 original publications focusing on anti-melanoma activity, photoprotection, and tissue regeneration. In vitro studies demonstrate that UA induces apoptosis in resistant melanoma cell lines (A375, HTB-140) via extrinsic/intrinsic pathways, with (−)-UA effectively overcoming doxorubicin resistance. Conversely, in non-cancerous models, low concentrations of UA accelerate wound and burn healing by upregulating vascular endothelial growth factor (VEGF), stimulating fibroblast proliferation, and optimizing extracellular matrix remodeling while preventing hypertrophic scarring. To mitigate skin sensitization and systemic risks, advanced drug delivery systems—including liposomes, nanoemulsions, chitosan nanogels, and electrospun scaffolds—have been developed, significantly enhancing skin permeability and localized dermal retention. Ultimately, the development of bio-functionalized smart dressings and targeted nano-formulations represents the most viable path toward unlocking the full clinical potential of UA in modern dermatological and oncological care. Full article

The innate–adaptive immune interface is a decisive control point determining whether therapeutic interventions induce durable protection, antitumor immunity, inflammatory, or immune tolerance. Many immunotherapies fail in translation because immunity is often treated as a single-output system rather than a spatially and temporally organized network shaped by tissue context, antigen-presenting cell fate, biomolecular conditioning, and metabolic state. This review introduces the immunoscape framework as a nanobiotechnology-oriented model for linking immune-state mapping with controllable translational variables, including delivery route, release kinetics, first-contact immune cells, lymphatic routing, biomolecular corona identity, antigen-presenting cell fate, and safety-gate assessment. Unlike systems immunology, which primarily describes immune networks, or conventional immune engineering, which often focuses on selected payloads, targets, or platforms, the immunoscape framework provides a design layer for predicting context-dependent immune outcomes. We discuss two converging strategies for reprogramming this interface: natural biologics, including beta-glucans, polyphenols, microbial metabolites, and extracellular vesicles; and smart nanomaterials, including lipid nanoparticles, biomimetic vesicles, lymph node-targeted platforms, and stimulus-responsive nanoarchitectures. We further propose translational design rules to guide clinically realistic immune-reprogramming nanomedicines for cancer, infectious, inflammatory, and regenerative applications. Full article

With the rise of Industry 4.0, digital manufacturing and smart measuring technologies are enabling the development of zero-defect manufacturing strategies, which leads to less material waste and lower energy consumption, moving from off-line metrology and dedicated measuring equipment to in-line measurements and automated inspection systems. This is especially important for the production and manufacturing of large-scale parts, because of the high component cost and long delivery cycle. However, establishing traceability for measurement systems is often complicated due to both the measurement technology and the objects being measured. Traceability of measurement in the manufacturing environment is not ensured yet, and uncertainty evaluation for in-process measurement remains a complex and active research challenge. This work introduces a new uncertainty modelling and evaluation framework for traceable measurement of the large-scale components in ‘shop floor’ conditions. The framework is verified using real data obtained from various instruments for in situ measurement of a large artefact. Experimental results demonstrate that uncertainty evaluation for large-scale metrology is crucial for precision manufacturing on the production floor. The methods can be extended to the evaluation of measurement uncertainty of components with a smaller size and off-line inspection. Full article

Existing truck–drone collaborative routing models predominantly assume fixed flight speeds, overlooking the non-linear coupling among speed, payload, and energy consumption, which limits urban delivery efficiency. To bridge this gap, this paper proposes the multiple flying sidekick traveling salesman problem with variable drone speed (mFSTSP-VDS). Formulating drone cruising speed as a continuous variable under strict non-linear energy constraints, we design a hybrid algorithm (ALNS-SA-VND) to jointly optimize routing, task allocation, and speed. Empirical analysis of Wuhan’s road network demonstrates the VDS strategy’s robustness. Specifically, VDS reduces the system makespan by up to 17.5% compared to rigid maximum-speed strategies, with consistent stability across varying load scenarios. By adaptively trading permissible battery capacity for temporal synchronization, VDS effectively mitigates unnecessary truck waiting times at rendezvous nodes. This study quantitatively validates the impact of sortie-specific speed adaptation on time efficiency, providing an exploratory theoretical baseline for tactical-level planning in smart logistics networks. Full article

Smart contact lens sensors integrate biochemical sensing elements, flexible electronics, power modules, and wireless readout components onto optically transparent contact lens platforms, enabling non-invasive and potentially continuous analysis of tear-derived biomarkers and ocular physiological signals. This review focuses on the translation pathway from contact lens materials and fabrication methods to sensing mechanisms, tear biomarker interpretation, and clinical deployment. We synthesize recent progress in substrate engineering, manufacturing processes, power delivery, and representative sensing strategies for intraocular pressure, glucose, electrolytes, pH, cortisol, cholesterol, and inflammatory cytokines. Instead of treating these systems as isolated examples, we compare optical/colorimetric, electrochemical, field-effect transistor, microfluidic, and wireless resonant approaches in terms of sensitivity, response time, power/readout requirements, and clinical relevance. Finally, we discuss persistent barriers, including biocompatibility, interface stability, tear-sample variability, calibration, sterilization, regulatory validation, data privacy, and compatibility with commercial contact lens manufacturing. Full article

UAV-based remote sensing systems are increasingly deployed in smart surveillance, disaster response, environmental monitoring, and critical infrastructure inspection. In these applications, aerial sensing platforms must transmit telemetry, control commands, and observation data securely and reliably under strict latency, energy, and computational constraints. However, existing security approaches often fail to jointly provide lightweight payload confidentiality, quantum-resilient key establishment, and adaptive communication protection suitable for dynamic and resource-constrained aerial sensing environments. To address this challenge, this paper proposes an energy-aware post-quantum hybrid cryptographic framework for secure and low-latency UAV remote sensing communications in UAV–IoT mission networks. The proposed framework integrates Ascon-based authenticated encryption for low-overhead protection of remote sensing payloads and mission telemetry, ML-KEM-based post-quantum session-key establishment for long-term resilience against quantum-era threats, and an AI-driven adaptive rekeying mechanism that dynamically adjusts key-refresh decisions according to threat level, residual energy, mobility state, channel stability, anomaly density, traffic sensitivity, link type, and mission progression. Accordingly, rekeying is treated not as a static maintenance process but as an intelligent and context-aware cryptographic control function that adapts communication security to evolving mission and sensing conditions. The framework is evaluated across twenty progressively demanding scenarios involving different UAV counts, sensor densities, payload sizes, communication modes, and adversarial settings relevant to real-time remote sensing operations. Experimental results demonstrate a secure delivery rate of 99.2%, attack detection and mitigation effectiveness of 98.9%, end-to-end encryption latency of 8.7 ms, throughput of 5.03 Mbps, energy overhead of 11.6 mJ/session, rekeying overhead of 2.9 mJ/event, session resilience of 96.4%, and integrity verification success of 99.1%. These findings show that the proposed framework provides a practical and scalable contribution to post-quantum secure UAV remote sensing by unifying lightweight authenticated encryption, ML-KEM-based quantum-resilient key establishment, and AI-driven adaptive rekeying within a resilient aerial–terrestrial communication architecture. Full article

Medication dispensing errors remain a significant concern in healthcare systems, particularly in elderly care and long-term medication management, where incorrect medication delivery may compromise patient safety and treatment outcomes. This study presents the design and experimental validation of a cyber–physical medication dispensing platform integrating robotic manipulation, edge AI-based visual verification, distributed motion control, and cloud synchronization. The platform combines a rotary medication storage mechanism, vacuum-based pill handling, a Klipper-based control framework, and a YOLOv8 perception subsystem deployed on a Hailo AI accelerator for real-time edge inference. Experimental evaluation was conducted under controlled laboratory conditions. Using an environment-specific validation dataset, the perception subsystem achieved a precision of 0.627, recall of 0.739, and mAP@0.5 of 0.786. An adaptive verification strategy was subsequently evaluated to improve dispensing verification under varying pill occupancy conditions. End-to-end system testing comprising 80 dispensing trials achieved an overall dispensing success rate of 86.25%, with no incorrect dispensing events observed. The results demonstrate the feasibility of integrating edge AI verification, distributed control, and cloud connectivity within a cyber–physical medication dispensing platform. The presented system provides a foundation for future research on perception-assisted medication dispensing, long-term deployment, and clinical validation in smart healthcare environments. Full article

Dissolving microneedles (DMN) could be considered as a minimally invasive alternative for transdermal delivery of naltrexone hydrochloride (NTX). In the present study, DMN patches with smart design were developed via a two-step micromoulding technique. The systems were composed of drug-free polyvinylpyrrolidone (PVP) and polyvinyl alcohol (PVA) blend microneedle tips, combined with a drug-loaded backing layer based on PVP and Poloxamer 407. The influence of polymer concentration in DMN tips and backing-layer composition on morphology, mechanical properties, drug release and permeation was evaluated. Mechanical studies revealed that intermediate polymer concentration (formulation MN-20%/2:1) provided superior structural integrity (13.57 ± 1.43% height reduction after compression) and efficient penetration up to the fourth Parafilm^® layer. Incorporation of NTX into the backing layer allowed for high drug loading, while a 2:1 PVP:P407 ratio provided higher toughness (1806 g/mm) as well as thermoresponsive and controlled drug release. In vitro permeation studies demonstrated significantly enhanced NTX delivery from DMN systems compared to simple matrix patches—an almost 4-fold increase in flux with 56% permeation of NTX up to 8 h. These findings highlight the importance of polymer composition in DMN design and demonstrate the potential of the developed systems as an effective platform for transdermal delivery of NTX. Full article

Background: Oral protein delivery is a major challenge in the field of pharmaceutical technology due to poor stability and limited permeability through intestinal barriers. Buccal delivery is a promising alternative with less restricting physiological conditions; however, low protein permeability is still a limiting factor. Multiple nanocarriers have been proposed to improve buccal protein delivery with lipid–polymer hybrid nanoparticles (LPHNs) combining the advantages of both polymeric and lipid-based systems. However, these conventional carriers rely on passive protein protection and lack adaptive release mechanisms. Objectives: This work aimed to develop and systematically optimize an ionic strength-responsive LPHN system that can minimize protein release in buccal ionic conditions while offering a triggered release in plasma after absorption. Methods: LPHNs were prepared by a two-step approach where polymeric cores of Eudragit-L100 were prepared by electrostatic complexation with Lysozyme (LYZ) followed by lipid shell formation by the ethanol injection method. Systematic optimization was performed using two-level factorial and central composite designs. Moreover, the ionic strength responsiveness and in vitro LYZ release were investigated in different ionic strength media. Results: The final optimized formulations, LPHNs and sodium deoxycholate-containing LPHNs (NaDC-LPHNs), exhibited a particle size of 257.2 ± 1.5 nm and 246 ± 5.7 nm, encapsulation efficiency of 69.89 ± 0.22% and 68.14 ± 0.16%, and high drug loading efficiency of 24.11 ± 0.06% and 23.65 ± 0.04%, respectively. Moreover, both formulations showed minimal protein release at low ionic strength (buccal-like) conditions while demonstrating a triggered release at higher ionic strength (plasma-like) conditions. Conclusions: The developed system may provide a promising smart strategy to improve buccal protein delivery by enhancing buccal protection and improving systemic delivery. Full article

Synthetic biology seeks to build predictable, programmable biological systems. We developed a blue-light-inducible T7RNAP system with dual-input regulation to enable precise spatiotemporal gene control, which is vital for biomanufacturing, therapy, and microbial engineering. We optimized it by replacing RBS sequences, testing tandem T7 promoters, and evaluating split-T7RNAP variants. Expression and bactericidal efficacy were assessed via fluorescent output and real-time growth curves under blue light. RBS variants caused up to 50-fold differences in expression. Three tandem T7 promoters provided the best balance between yield and fidelity. Integration of a benzoate-responsive module enabled 4.5-fold repression at 3 mM benzoate, demonstrating effective chemical off-switching without compromising light induction. This system combines blue light precision with environmental responsiveness, offering non-invasive, on-demand activation for antimicrobial therapy or spatial bioproduction. The benzoate-triggered off-switch is especially valuable for ecological applications such as biocontainment or bioremediation, where gene expression must shut down upon detection of pollutants, for example, aromatic hydrocarbons. Its orthogonal, modular design supports context-dependent control, making it ideal for environmental biosensors, programmable probiotics, and smart antimicrobial delivery in complex ecosystems. Full article

Poor indoor air quality, inadequate ventilation, and unnoticed local disturbances can reduce occupant well-being and compromise practical safety in smart-home and small-building environments. Although low-cost Internet-of-Things (IoT) sensing technologies are widely available, many monitoring systems remain focused on single-modality sensing and do not jointly evaluate environmental conditions, vibration activity, communication reliability, and gateway-side interpretation within one framework. This study presents the design, implementation, and proof-of-concept evaluation of a low-cost, privacy-conscious, non-imaging IoT-based indoor environment and safety-awareness monitoring framework built with ESP32/Arduino sensor nodes and a Raspberry Pi gateway. The system integrates carbon dioxide, temperature, humidity, gas-resistance/VOC-trend indication, and vibration sensing with MQTT-based communication and edge-side analytics. Controlled subsystem experiments showed that CO₂ concentration differentiated ventilation conditions, increasing from 395.47 ppm in the valid empty/open-door baseline to 1083.16 ppm in the closed occupied condition. Vibration states were distinguished using root-mean-square acceleration features across calm, surface-disturbance, footstep, play, and jump conditions. MQTT evaluation using 1000-message batches showed no observed message loss or duplicates across the tested QoS/network combinations, although latency and throughput varied by network configuration and QoS level. QoS 1 provided a practical balance between low latency and protocol-level delivery assurance in the tested local/Wi-Fi setting. A final integrated validation run further demonstrated synchronized acquisition from indoor environmental, vibration, and outdoor CO₂ reference publishers through the same Raspberry Pi gateway, with zero missing or duplicate sequence flags across the three streams. Overall, the findings indicate that lightweight open-source IoT hardware can support a reproducible building-level sensing and edge-analytics prototype for indoor environment and safety-awareness monitoring. Broader deployment in standard-sized rooms, multi-room buildings, and smart-city infrastructure remains future work. Full article

Successful project delivery largely depends on effective progress management to ensure schedule reliability and resource efficiency. Conventional manual and paper-based approaches remain inefficient and error-prone, often causing fragmented data and poor collaboration among stakeholders. To overcome these limitations, this study proposes a computer vision–blockchain integrated framework for closed-loop construction progress management within the Plan–Do–Check–Act (PDCA) cycle. This system supports an automated, end-to-end workflow in which UAV-captured images are processed by a computer vision model, digitally signed, and verified on a blockchain ledger, triggering smart contract-based schedule deviation alerts to relevant stakeholders. An enhanced digital signature scheme ensures data integrity during off-chain and on-chain transitions, while self-executing smart contracts coordinate schedule submissions, progress reporting, and deviation detection. Implemented on Hyperledger Fabric and validated through a case study, the framework demonstrates transparent data flow and strong performance in detection accuracy, latency, and throughput. By shifting progress management from passive reporting toward proactive control, this study provides a replicable, transparent, and tamper-resistant solution for multi-stakeholder construction progress governance. Full article

Global environmental challenges—ranging from climate change to resource depletion—require not only technological innovation but also sustained shifts in household behavior. Two principal policy tools have emerged to promote such shifts in residential communities: financial incentives (e.g., subsidies, rebates, dynamic pricing) and educational campaigns (e.g., information provision, social norms messaging, feedback systems); yet rigorous comparative evidence on their relative intervention effectiveness —defined here as the magnitude of behavioral change achieved—remains fragmented. The aim of this review is to systematically compare the effectiveness of financial incentives and educational campaigns for promoting pro-environmental behaviors in residential communities, and to identify the conditions under which each approach performs best. This systematic review addresses: How do financial incentives compare to educational campaigns in promoting pro-environmental behaviors in residential communities? Through PRISMA 2020 methodology, synthesizing 51 studies including 5 major meta-analyses (2015–2024), comparative intervention effectiveness evidence is provided. Financial incentives achieve modest reductions (1.8–6.0%, g = 0.36) with rapid adoption but substantial rebound effects (35–60% offset) and poor persistence post-removal. Educational campaigns show higher variability (g = 0.23 to 0.93), with targeted approaches achieving up to 8% reductions, better persistence (57% effect retention at 24 months), and lower rebounds (15–30%). Combined approaches demonstrate the largest effects (g = 0.64) and optimal cost-effectiveness. Context determines effectiveness: financial incentives excel for high-cost technology adoption; and educational campaigns for habitual behaviors. Technology-mediated delivery (smart meters, mobile apps) enhances both approaches. The principal contribution of this review is a comprehensive umbrella synthesis to directly compare both intervention paradigms while simultaneously accounting for rebound effects, moral licensing, age-specific moderators, and cost-effectiveness, offering practitioners an integrated evidence base for intervention selection. We conclude with evidence-based recommendations for intervention selection. Full article

Vascular–osteogenic coupling plays a central regulatory role in bone regeneration, but it is frequently impaired under pathological conditions, including aging, ischemia, and chronic inflammation, which compromises efficient bone repair. Gelatin methacryloyl (GelMA) hydrogels, which combine extracellular matrix-like bioactivity, adjustable mechanical properties, and compatibility with three-dimensional biomanufacturing, have become a widely used material platform for vascularized bone regeneration. From the perspective of vascular–osteogenic coupling, this review reframes and synthesizes GelMA-based approaches for vascularized bone regeneration, grouping existing strategies into three categories: (i) intrinsic material design, in which pore architecture, microchannels, dynamic networks, and interfacial functionalization are used to guide vascular ingrowth; (ii) exogenous bioactive delivery, involving growth factors, extracellular vesicles, cells, and inorganic ions to enhance vascularization; and (iii) smart responsive strategies, including ROS/pH-responsive systems, sequential release, and external stimulation, which aim to recapitulate the evolving microenvironment during bone repair. This review further compares these strategies in terms of evidence level, reproducibility, and translational potential. Exogenous delivery systems currently have the strongest preclinical support, but issues related to dose standardization, burst release, and long-term safety remain unresolved. Intrinsic material programming is less extensively studied, yet may be more compatible with manufacturing consistency, sterilization, and engineering translation. Most stimuli-responsive systems, by contrast, remain largely at the small-animal or proof-of-concept stage. Future GelMA-based systems should therefore shift from increasing functional complexity toward improving predictability, reproducibility, and clinical feasibility. Compositionally defined and structurally controllable GelMA composites that integrate vascular regulation with mechanical support may provide a more realistic path for vascularized bone regeneration. Full article

Electric vehicle (EV) charging networks face major operational challenges, including demand uncertainty, peak-load congestion, and anomalous charging behavior, particularly in multi-station environments. This study proposes an anomaly-aware Q-learning framework for real-time scheduling in multi-station EV charging systems by integrating short-term load forecasting, anomaly detection, and intelligent scheduling within a unified operational pipeline. The framework combines Prophet, XGBoost, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models for short-term demand forecasting, while Convolutional Neural Networks (CNN), Autoencoders, and Isolation Forests are employed for anomaly detection. Forecasting and anomaly information are incorporated into a Q-learning scheduler to support adaptive charger allocation and congestion management. Evaluation using a four-year, real-world dataset comprising more than 2000 EV charging sessions demonstrates improved scheduling performance, achieving reductions in peak load and waiting time while improving energy delivery consistency. The framework further demonstrates low scheduling latency, supporting suitability for real-time deployment in OCPP-compliant smart charging infrastructures. Full article