Search Results (394)

Understanding mass transport of structurally different drugs within ionically crosslinked hydrogel networks remains an important challenge in polymer-based delivery systems. In this study, hydrophilic amoxicillin (AMOX) and amphiphilic doxycycline (DOX) were encapsulated into calcium–alginate beads, respectively. A three-factor and three-level Box–Behnken design was utilized to examine the influences of alginate concentration (2–5%, w/v), CaCl₂ concentration (1–3%, w/v), and gelation time (15–45 min) on encapsulation efficiency (EE). EE exhibited considerable variability for both AMOX (10–86%) and DOX (10–63%). Optimal EE values were achieved at almost 3.5% alginate and 3% CaCl₂. The optimized gelation times differed between AMOX (45 min) and DOX (15 min), which is likely associated with differences in their physicochemical properties, although additional intermediate gelation times could further refine the optimal conditions. ANOVA identified CaCl₂ concentration and the quadratic effect of alginate as the most influential parameters. Furthermore, both models demonstrated robust predictive capability (R² > 0.98). In vitro release experiments demonstrated minimal drug diffusion in simulated gastric fluid (SGF) and significantly accelerated release in simulated intestinal fluid (SIF). These findings indicate a pH-responsive release behavior under simulated gastrointestinal conditions. The release profile was best represented by Higuchi and Korsmeyer–Peppas kinetic models. SEM and optical microscopy revealed uniform spherical beads with drug-dependent microstructural differences: hydrophilic AMOX produced smoother, wrinkled surfaces, whereas amphiphilic DOX induced localized cracking and heterogeneous microdomains. Furthermore, DLS and zeta potential measurements of the released fractions indicated nanoscale particle populations (≈190–225 nm) with moderate negative surface charge (≈−21 mV), suggesting stable colloidal dispersion during intestinal-phase release. Full article

Energy transitions depend on coordination across interdependent actors, infrastructures and practices. As electrification, sector coupling and decentralisation reshape energy systems, governance challenges have shifted from control-oriented delivery towards problems of interaction, responsibility and system coordination, yet governance arrangements continue to struggle with persistent coordination failures across institutional contexts. This article argues that such difficulties cannot be explained as implementation problems or insufficient cooperation alone. Coordination failures are conceptualised as manifestations of a mismatch between how energy systems function and how they are governed, distinguishing supply-chain governance assumptions from energy systems operating as service networks. The analysis shows how prevailing governance remains oriented towards linear responsibility and asset-based performance, even as outcomes increasingly depend on coordinated action and resource integration in use. Network-based arrangements represent partial adaptations that acknowledge interdependence without reconfiguring underlying governance logics. Building on this diagnosis, three design principles—relational accountability, alignment-based performance measurement, and constitutive coordination—clarify what a genuine shift in governance logic would entail, as distinct from adaptive overlays. The framework is illustrated through regional energy governance experiences in Sweden and comparable European settings, clarifying why coordination failures persist in sustainability transitions characterised by complex socio-technical interdependence. Full article

Structural health monitoring (SHM) systems based on Internet of Things (IoT) technologies have become an effective approach for continuous monitoring of bridge infrastructures. However, many wireless monitoring systems relying on LoRaWAN or contention-based communication suffer from packet collisions, unpredictable latency, and limited scalability when multiple sensing nodes operate simultaneously. To address these limitations, this study proposes a soft real-time LoRa-based IoT architecture for bridge SHM using a time division multiple access (TDMA) communication framework implemented on an embedded real-time platform. The proposed system integrates distributed vibration sensing nodes, a TDMA-enabled LoRa communication layer, an ESP32-based gateway, and a web-based monitoring database for remote visualization and analysis. The architecture leverages FreeRTOS (v10.4.3) for system-level task scheduling, enabling concurrent execution of sensing, communication, and networking processes across the dual-core ESP32-WROOM-32D platform. Experimental results obtained using a laboratory-scale cable-stayed bridge model demonstrate stable multi-node communication with a packet delivery ratio exceeding 95% and predictable TDMA-scheduled transmission cycles with TDMA slots of 100–200 ms under the evaluated operating conditions. The experiments validate end-to-end operation using a representative three-node deployment, while broader scalability is evaluated analytically through the TDMA capacity model and identified as future work for larger physical deployments. Full article

Pancreatic ductal adenocarcinoma is highly malignant with poor prognosis. Its dense tumor microenvironment severely limits the efficacy of conventional chemotherapy and causes severe side-effects. Herein, we adopt the established Schiff-base crosslinked thermoresponsive injectable self-healing poly(2-(2-methoxyethoxy)ethyl methacrylate-co-oligo(ethylene glycol) methyl ether methacrylate-co-aldehyde 2-hydroxyethyl methacrylate)/carboxymethyl chitosan (APMOH/CMCS) hydrogel as the delivery scaffold. By regulating monomer composition, the volume phase transition temperature (T_VPT) of the hydrogel was tuned to around 43 °C to match the therapeutic temperature requirement. Subsequently, copper–metal organic framework (Cu-MOF) nanoparticles co-loaded with 2,2′-azobis(2-methylimidazoline) dihydrochloride (AIPH) and 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) cationic radicals (ABTS·⁺) (denoted as AB@Cu-MOF) were uniformly incorporated into the hydrogel network. Under near-infrared (NIR) irradiation, ABTS·⁺ acts as a photothermal agent to generate hyperthermia for tumor ablation; the elevated temperature further activates AIPH to produce alkyl radicals, which can oxidize inactivated ABTS back to ABTS·⁺ and construct a sustainable photothermal therapy–thermodynamic therapy (PTT-TDT) circulation. Meanwhile, Cu-MOF can consume intracellular glutathione (GSH) to protect active components from deactivation and initiate chemodynamic therapy (CDT) via Fenton-like reactions to produce toxic reactive oxygen species. Benefiting from the thermoresponsive characteristic, the hydrogel undergoes volume shrinkage upon heating, achieving NIR-triggered on-demand drug release with a cumulative release rate of 81.1%. In vitro and in vivo experiments verified that this integrated platform realizes remarkable triple synergistic efficacy of PTT, TDT, and CDT. The tumor volume of the treatment group was merely 13.3% of the control group, and the system also exhibited excellent biocompatibility. Collectively, it offers a feasible and promising intelligent platform for precise local treatment of pancreatic cancer. Full article

Background: Culturally and Racially Marginalised (CaRM) communities in Australia encounter subtle and covert forms of prejudice, commonly referred to as “new racism”. Within healthcare settings, these experiences can shape trust, engagement, and patterns of help-seeking. Mental health nurses are often the first point of contact in care delivery, and their ability to recognise, respond to, and mitigate the impacts of new racism is critical for fostering therapeutic relationships and supporting equitable access. Understanding how CaRM communities perceive the conditions that influence their mental health service use is fundamental for informing more equitable and culturally responsive care. Objective: This study explored the viewpoints of CaRM community members regarding the factors they consider important for addressing new racism in healthcare systems and supporting engagement with mental health services. Design: Q methodology was used to identify statistically derived viewpoints that reflect shared viewpoints about the conditions perceived as critical for addressing the impacts of new racism on mental health service use. Setting: Participants were recruited from culturally and linguistically diverse communities across Australia through community settings, social media, and professional networks. Participants: Thirty-five individuals from CaRM backgrounds completed the Q-sort. Methods: This Q methodology consisted of five steps: (1) set up of the Q-sorting instrument, (2) selection of participants, (3) data collection, (4) factor analysis, and (5) factor interpretation. Results: Three distinct viewpoints were identified: (1) raising awareness of mental health issues within CaRM communities (community-focused), (2) providing visible anti-racism and culturally safe services (service-focused), and (3) recognising and formally addressing new racism within healthcare systems (policy-focused). Conclusions: This study offers the first empirically derived, community-informed set of viewpoints on addressing new racism in Australian mental healthcare. While exploratory, the findings highlight multi-level considerations that are potentially relevant to mental health nursing practice, and may be useful to inform future research, policy development, and service redesign aimed at strengthening cultural responsiveness and equity in mental health systems. 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

Background/Objectives: Quality improvement (QI) is widely used in global health to improve patient outcomes, reduce costs, and strengthen service delivery. The Texas Children’s Global Health Network (TCGHN) includes nine independent non-governmental organizations supporting healthcare in low- and middle-income countries (LMICs), with pediatric HIV clinical centers of excellence in six countries in sub-Saharan Africa (SSA), supported technically by Baylor College of Medicine. We describe the development of a virtual QI Community of Practice (QICoP) to connect geographically dispersed teams and strengthen local QI capacity. Methods: In 2022, QI and global health experts convened to design the QICoP and assess site readiness. Participants were recruited from the sites based on their interest. Meetings were held via Zoom, with attendance, evaluations, and organizer notes tracked. QI tools were used to identify site strengths, challenges, and strategies to improve engagement. Results: From January 2023 to September 2024, the QICoP held 15 sessions, including 3 abstract-writing workshops, averaging 35 participants per session. QI abstract submissions to the annual Network meeting doubled from 2023 to 2024. Across 15 sessions, 83% of participants reported positive experiences. Based on participant feedback and QI sessions from the 2022–2024 Network meetings, we developed a blended QI basics curriculum, recruited site champions to improve communication, and launched a WhatsApp platform to enhance engagement. Conclusions: A virtual QICoP may be a feasible model to support professional development, increase knowledge and idea sharing, and connect individuals across geographies over a shared mission to improve healthcare quality in LMICs. Full article

Background/Objectives: Cannabis sativa is the source of cannabidiol (CBD), a non-intoxicating phytocannabinoid with analgesic and anti-inflammatory qualities that has demonstrated therapeutic potential in inflammatory skin conditions like dermatitis. However, low bioavailability and poor water solubility restrict its topical application. This study attempted to improve CBD solubility and transdermal delivery using an amorphous solid dispersion (ASD)-based hydrogel system. Methods: CBD was stabilized in its amorphous form using an ASD strategy and incorporated into a hydrogel matrix. The CBD-ASD hydrogel was characterized by particle size analysis, scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FT-IR), rheological assessment, swelling studies, and diffusion experiments using Franz cells. Biological evaluations included cytotoxicity testing in human dermal fibroblast (HDF) cells, wound-healing assays, RT-qPCR-based anti-inflammatory analysis, antioxidant activity (DPPH assay), and antibacterial testing against Staphylococcus aureus. Results: Physicochemical analyses confirmed successful amorphous dispersion of CBD within a stable hydrogel network. The formulation exhibited sustained drug release over 144 h, achieving 86.32% cumulative release with diffusion-controlled kinetics. Rheological and swelling properties demonstrated mechanical stability and hydration suitability for long-term topical application, while Franz diffusion studies confirmed effective transdermal permeation. The CBD-ASD hydrogel showed no cytotoxicity in HDF cells and significantly enhanced wound closure. It also downregulated pro-inflammatory cytokines including interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α). Additionally, the formulation demonstrated 65.63 ± 10.00% DPPH radical scavenging activity and over 99% antibacterial inhibition. Conclusions: The CBD-ASD hydrogel represents a stable, multifunctional delivery platform that overcomes CBD solubility limitations and enhances therapeutic efficacy for inflammatory skin diseases. Full article

Embedding large language model (LLM) coordinators in production electronic systems, connected vehicles, multi-robot fabrics, IoT control loops, telecommunications orchestration, demands a pre-delivery filter stage that preserves ethical guarantees under adversarial influence at deployment scale. We present a constitutional governance layer that filters compiled influence policies before they reach a heterogeneous population of grounded LLM agents whose hybrid decision model combines a game-theoretic base probability with an LLM-evaluated narrative shift attenuated by per-agent resistance. Four experiments on a Barabási–Albert scale-free network of 30 agents powered by Llama-3.3-70B-Instruct show that the filter holds an Ethical Cooperation Score (ECS) of $0.176$ (multi-seed mean $0.163$, 95% confidence interval (CI) $[0.150,0.174]$) against an unconstrained baseline of $\mathrm{ECS}=0$, enforced by a hard integrity gate ($1.000$ vs. $0.000$). We surface an autonomy paradox in which unconstrained agents resist manipulation more forcefully ($0.856$ vs. $0.728$) yet collapse to $\mathrm{ECS}=0$, establishing that system-level integrity cannot be delegated to agent-level defence. The advantage is monotonic in resistance ($+0.174$ to $+0.183$), seed-stable (Cliff’s $\delta =1.0$, complete separation), topology- and backbone-invariant across five contemporary LLMs, robust to alternative ECS formulations, and reproduces at N = 100. Against constitutional artificial intelligence (CAI) critique-revise and LlamaGuard-style safety-classifier baselines, the framework matches the integrity floor and adds a measurable margin on the secondary risk surface (burst timing, composite manipulation risk). The filter runs at 0.78 μs/call ($\approx 1.3\times {10}^6$ decisions/s/core), supporting always-on deployment as a stateless, model-agnostic component of LLM agent pipelines in adversarially contested electronic systems. Full article

Water structures play a vital role in regulating irrigation water within open-channel networks by controlling discharge, water levels, flow direction, and velocity. Despite their importance, these structures act as hydraulic obstructions that induce flow disturbances, which may reduce hydraulic efficiency and threaten structural integrity. One of the most critical consequences is localized erosion downstream, posing serious risks to structural safety and long-term performance. From a sustainability perspective, maintaining structural stability and hydraulic efficiency is essential to ensure reliable water delivery, minimize maintenance costs, and extend the service life of irrigation structures. Therefore, mitigating such adverse hydraulic effects is a key component of sustainable water resources management. This study aims to investigate the mechanisms responsible for this phenomenon and propose engineering solutions to reduce its impacts. The geometry of upstream wing walls significantly influences flow behavior both through and downstream of the structure. Additionally, irrigation canals are constructed with varying side slopes depending on soil conditions, which further affect flow characteristics. However, the combined effect of different upstream wing wall configurations and canal inside slopes has not been sufficiently addressed. Accordingly, this research evaluates their integrated impact to support the development of more efficient, resilient, and sustainable irrigation structures. A total of 435 laboratory experiments were conducted using a physical model under varying discharge conditions. Common canal inside slopes were tested with four widely used wing wall types. Scour hole geometry, including depth, length, and shape, was measured and analyzed. Results indicate that the splayed wing wall configuration outperforms the box type, reducing maximum scour depth and length by approximately 22.74% and 23.61%, respectively, when combined with a 1:1 canal inside slope. Additionally, new dimensionless empirical equations were developed to predict downstream scour behavior, providing practical tools for selecting optimal wing wall configurations under different canal conditions. Full article

The rapid growth of e-commerce has significantly increased direct-to-consumer deliveries, putting competitive and environmental pressure on urban last-mile logistics. Out-of-home (OOH) delivery options, particularly parcel lockers, are increasingly integrated into city mobility strategies to reduce congestion and emissions. However, the role of mobile applications front-ending these networks remains under-researched. This study aims to evaluate the user experience (UX) and functional adequacy across three major parcel-locker apps in Poland: InPost Mobile, DPD Mobile, and ORLEN Paczka. A cross-sectional, mixed-methods approach combining in situ corridor testing and structured post-task questionnaires was employed with 30 users at real locker locations in Katowice. The results indicate that interface simplicity, predictable information flow, and technical stability are the dimensions most consistently associated with higher user ratings. InPost Mobile consistently achieved the highest ratings due to its focus on core workflows, whereas applications emphasizing broader functional coverage (ORLEN Paczka) exhibited usability trade-offs, and DPD Mobile underperformed in speed and stability. Because the study relied on a small convenience sample (n = 30) in a single city and was skewed toward younger adults (18–24), the findings should be interpreted as exploratory and primarily reflective of a digitally proficient demographic rather than the broader user population. Full article

Background: Efficient carrier selection and routing in multimodal logistics networks is increasingly complex due to operational uncertainty, fluctuating service reliability, and the need for disturbance-aware decision-support tools. This study develops an integrated optimization framework for simultaneous carrier selection and routing under operational disturbances. Methods: A disturbance-aware multi-criteria network optimization model is proposed that incorporates transportation cost, delivery time, reliability, and economic risk within a unified mathematical formulation. Operational disturbances are represented as parametric perturbations of network costs, enabling recalculation of routing decisions. Computational experiments were conducted on an illustrative multimodal network and additional synthetic networks. Results: The results show that routing decisions remain stable under disturbance levels up to 5% and reconfigure under higher perturbations. Comparative analysis with a classical cost-minimization routing model illustrates a potential reduction in expected economic risk exposure of approximately 25–30% within the illustrative experimental setting while maintaining comparable transportation time. Conclusions: The proposed framework integrates carrier selection and routing decisions in multimodal logistics systems and supports risk-aware decision-making under operational uncertainty. Full article

Flying Ad Hoc Networks (FANETs) composed of multiple unmanned aerial vehicles (UAVs) are a promising solution for emergency wireless communications when terrestrial infrastructure is unavailable. However, ensuring reliable Quality of Service (QoS) in these highly dynamic networks remains challenging due to topology changes, varying propagation conditions, and congestion. This work proposes a lightweight machine learning-based QoS optimization framework for multi-UAV emergency communications that combines realistic mobility modeling, empirical channel measurements, and adaptive traffic prioritization. UAV mobility patterns are generated with ArduSim, while LoS/NLoS propagation models are derived from real UAV flight experiments and integrated into ns-3. Multiple supervised machine learning algorithms—including Decision Trees, Random Forest, Support Vector Machines, k-NN, Gradient Boosting, and CatBoost—are trained using four input features derived from the network state: ${\mathrm{CBR}}_{\mathrm{src}}$, ${\mathrm{QP}}_{\mathrm{src}}$, ${\mathrm{CBR}}_{\mathrm{dst}}$, and ${\mathrm{QP}}_{\mathrm{dst}}$. Simulation results show that the proposed AI SMOTE EMERGENCY scheme, based on CatBoost, improves the Packet Delivery Ratio (PDR) by approximately 43% over the No-QoS baseline, achieving 89–93% delivery across all four application ports. Compared with EDCA, the proposed scheme maintains reliable delivery for all services, increases emergency throughput by 34–36%, and reduces end-to-end delay by about 70%. In addition, the higher delivery reliability translates into clear communication energy benefits, reducing energy waste across all evaluated topologies when compared with the No-QoS baseline. The inference time remains below 0.002 s, supporting real-time QoS adaptation in resource-constrained UAV networks. Full article

Delivery services have drawn much attention and become of topmost significance in urban areas by presenting online food delivery selections for a diversity of dishes from a wide range of restaurants, decreasing both travel and waiting times. Customer data analysis acts as a cornerstone in corporate strategy, allowing enterprises to gather and interpret user feedback and helping them to make informed decisions that drive future business development. However, major knowledge gaps remain due to the scarcity of literature review studies on these delivery services, hindering a complete understanding of customer satisfaction in this sector. Furthermore, there has been little systematic research on managerial response tactics to online consumer complaints and negative reviews. Researchers have contributed by applying artificial intelligence, including deep learning and machine learning models, to analyze customer sentiment and understand customer brand perceptions. This study presents a Hybrid Deep Neural Network Model for Customer Satisfaction Analysis (HDNNM-CSA), with the aim of developing an efficient model which is capable of accurately classifying customer satisfaction levels in delivery apps based on textual responses provided by customer experience managers. To achieve this, the model initially pre-processes text data using text cleaning, emoji removal, normalization, tokenization, stop word removal, and stemming to clean and standardize the unstructured text data for further analysis. Following this, term frequency–inverse document frequency-based word embedding is utilized to transform the pre-processed text into meaningful feature representations. Lastly, an ensemble architecture involving bidirectional long short-term memory, temporal convolutional, and graph convolutional networks is deployed to classify customer satisfaction levels with managers’ responses. A series of experimental analyses are performed, and the results are examined for numerous features. A comparative analysis demonstrates the enhanced performance of the HDNNM-CSA technique with respect to existing approaches. Full article

Flood disasters often disrupt road networks and severely reduce ground accessibility, hindering the timely delivery of emergency supplies. To address this challenge, this study investigates a collaborative routing problem involving multiple vehicles and multiple UAVs under road disruptions and formulates a mixed-integer linear programming model that jointly minimizes mission makespan and priority-weighted response time for critical nodes. The model explicitly captures road feasibility, vehicle speeds affected by flood depth, multi-point UAV sorties, payload-dependent energy consumption, and vehicle–UAV spatiotemporal synchronization. To balance solution quality and scalability, a dual-track solution framework is developed: exact optimization is used for small instances, while a adaptive large neighborhood search algorithm with embedded dynamic programming is designed for larger instances. A case study based on the 2024 Guangdong flood with 135 demand points shows that the heuristic can obtain high-quality solutions efficiently and outperforms time-limited MILP solutions on large instances. Comparative experiments further demonstrate that multi-point sorties, integrated coordination, and embedded sortie refinement are all crucial to performance improvement. Sensitivity analysis indicates that setting the trade-off coefficient α within 0.2–0.8 provides a robust balance between overall mission efficiency and timely response to critical nodes. Full article