Search Results (478)

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

Wireless power transfer (WPT) has emerged as a compelling solution for delivering electrical energy without physical connectors, particularly in applications requiring reliability, mobility, or encapsulation. This work presents the modeling, simulation, construction, and experimental validation of an optimized dual-frequency WPT system using magnetically coupled resonant coils. Unlike conventional single-frequency systems, the proposed architecture introduces two independently controlled excitation frequencies applied to distinct transistors, enabling improved resonance behavior and enhanced power delivery across a range of coupling conditions. The design process integrates numerical circuit simulations in PSpice and three-dimensional electromagnetic analysis in ANSYS Maxwell 3D, allowing accurate evaluation of coupling coefficient variation, mutual inductance, and magnetic flux distribution as functions of coil geometry and alignment. A sixth-degree polynomial model was derived to characterize the coupling coefficient as a function of coil separation, supporting predictive tuning. Experimental measurements were carried out using a physical prototype driven by both sinusoidal and rectangular control signals under varying load conditions. Results confirm the simulation findings, showing that specific signal periods (e.g., 8 µs, 18 µs, 20 µs, 22 µs) yield optimal induced voltage values, with strong sensitivity to the coupling coefficient. Moreover, the presence of a real load influenced system performance, underscoring the need for adaptive control strategies. The proposed approach demonstrates that dual-frequency excitation can significantly enhance system robustness and efficiency, paving the way for future implementations of self-adaptive WPT systems in embedded, mobile, or biomedical environments. Full article

Background: Incarcerated patients with neurological complaints present substantial diagnostic and care-delivery challenges in emergency departments (EDs). We delineate the clinical spectrum, severity, and outcomes among incarcerated patients managed in an ED with an embedded neuro-emergency expert model. Methods: A retrospective observational study of adult ED visits for neurological symptoms was conducted from September 2018 to June 2025 at a government-designated regional emergency center serving multiple correctional facilities. Incarceration was confirmed in the electronic medical record. Extracted variables included demographics, chief complaint, comorbidities, triage and acuity scale, Glasgow Coma Scale (GCS), neuroimaging, ED diagnoses, and outcomes (hospital admission, ICU care, ED/in-hospital mortality). Results: Sixty-five patients were included (median age 57.0 years [IQR 47.0–64.5]; 95% male). Chief complaints were altered mental status (36.9%), hemiparesis (21.5%), and seizures (13.8%). On arrival, 40.0% had GCS ≤ 12, including 23.1% with severe impairment (GCS 3–8). Non-contrast head CT was obtained in 95.4% and diffusion-weighted MRI in 38.5%. Frequent diagnoses were psychiatric/functional neurological disorder (16.9%), metabolic encephalopathy (15.4%), and acute ischemic stroke (12.3%). Serious conditions (stroke, hypoxic brain injury, central nervous system infection, status epilepticus, and neuroleptic malignant syndrome) were diagnosed in 41.5%. Hospital admission occurred in 63.1% (ICU care in 47.7%); in-hospital mortality was 10.8%. Conclusions: ED visits by incarcerated individuals with neurological complaints were often linked to serious diagnoses, ICU use, and mortality, challenging assumptions of exaggeration. Over two in five had stroke, hypoxic brain injury, central nervous system infection, or status epilepticus. The findings support rapid, systematic, bias-aware evaluation with early neurological involvement, clear imaging triggers, safety protocol, and expedited transfers from correctional facilities. 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

The rapid expansion of fashion e-commerce has raised concerns over the environmental cost of last-mile deliveries, especially in pure-player models. This preliminary study examines the estimated carbon footprint of TENDAM’s omnichannel model—based on in-store pickup and returns—compared to pure-player home delivery, using a customer-level approach across 11 Spanish cities of varying sizes. A total of 3106 face-to-face surveys were conducted in TENDAM stores, capturing data on mobility behavior, transport modes, trip chaining, and service types. Emission factors were applied using a Python-based analytical model, and results were contrasted with Monte Carlo simulations from existing literature on pure players. Our findings indicate that the average per-service footprint of the omnichannel model is around 400 g ${\mathrm{C}\mathrm{O}}_2-\mathrm{e}\mathrm{q}$, significantly lower than the 1500–3000 g ${\mathrm{C}\mathrm{O}}_2-\mathrm{e}\mathrm{q}$ range for pure players. Emissions were especially low in large cities and in street-level stores, largely due to the high rate of walking and multipurpose trips among customers. The study also includes geospatial analysis through interactive influence maps. These results suggest that dense store networks embedded in walkable urban areas can substantially reduce last-mile GHG emissions. While preliminary, the study highlights the potential for omnichannel retail to support urban decarbonization goals and sustainability when integrated with sustainable mobility patterns. Full article

Safeguarding patient and personnel safety and improving care quality has emerged as a critical priority for healthcare systems globally. In response to persistent challenges in healthcare delivery, many countries have adopted clinical governance frameworks and organisational learning processes to strengthen accountability and promote continuous improvement. Robust clinical governance frameworks provide the processes and accountability measures necessary to foster a culture of knowledge-sharing and evidence-based decision-making, all of which are key characteristics of a learning organisation. This study seeks to investigate the role of clinical governance in improving hospital performance through three interconnected sub-studies. The first sub-study will explore how non-clinical managers in selected public sector hospitals leverage clinical governance to improve hospital performance. The second sub-study will evaluate the impact of clinical governance interventions on clinical outcomes and identify opportunities for organisational learning within these hospitals. The third sub-study will serve as an embedded experimental component, monitoring changes in complaint resolution indicators before and after interventions to assess improvements in clinical governance through both intra- and inter-hospital comparisons. Qualitative data will be analysed using NVivo version 15, with inductive thematic analysis employed to uncover emergent patterns and interpretive themes. Full article

Electronic Health Records (EHRs) have enhanced access to medical information but have also introduced challenges for healthcare providers, such as increased documentation workload and reduced face-to-face interaction with patients. To mitigate these issues, we propose RAGMed, a Retrieval-Augmented Generation (RAG)-based AI assistant designed to deliver automated and clinically grounded responses to frequently asked patient questions. This system combines a vector database for semantic retrieval with the generative capabilities of a large language model to provide accurate, reliable answers without requiring direct physician involvement. In addition to patient-facing support, the assistant facilitates appointment scheduling and assists clinicians by summarizing clinical notes, thereby streamlining healthcare workflows. Additionally, to evaluate the influence of retrieval quality on overall system performance, we compare two embedding models, gte-large and all-MiniLM-L6-v2, using real-world medical queries. The models are assessed within the RAG-Triad Framework, focusing on context relevance, answer relevance, and factual groundedness. The results indicate that gte-large, owing to its higher-dimensional embeddings, retrieves more informative context, resulting in more accurate and trustworthy responses. These findings underscore the importance of not only the potential of incorporating RAG-based systems to alleviate physician workload and enhance the efficiency and accessibility of healthcare delivery but also the dimensionality of models used to generate embeddings, as this directly influences the relevance, accuracy, and contextual understanding of retrieved information. This prototype is intended for the retrieval-augmented answering of medical FAQs and general informational queries, and is not designed for diagnostic use or treatment recommendations without professional validation. Full article

Illegal broadcasting is one of the primary challenges for Over the Top (OTT) service providers. Watermarking is a method used to trace illegal redistribution of video content. However, watermarking introduces processing overhead due to the embedding of unique patterns into the video content, which results in additional latency. End-to-end network latency, caused by network congestion or heavy load on the origin server, can slow data transmission, impacting the time it takes for the segment to reach the client. This paper addresses 5xx errors (e.g., 503, 504) at the Content Delivery Network (CDN) in real-world video streaming platforms, which can negatively impact Quality of Experience (QoE), particularly when watermarking techniques are employed. To address the performance issues caused by the integration of watermarking technology, we enhanced the system architecture by introducing and optimizing a shield cache in front of the packager at the origin server and fine-tuning the CDN configuration. These optimizations significantly reduced the processing load on the packager, minimized latency, and improved overall content delivery. As a result, we achieved a 6% improvement in the Key Performance Indicator (KPI), reflecting enhanced system stability and video quality. Full article

Rapid access to defibrillators, blood products, and time-critical medicines can improve survival, yet urban congestion and fragmented infrastructure delay deliveries. We present and evaluate an end-to-end framework for beyond-visual-line-of-sight (BVLOS) UAV logistics in Essex (UK), integrating (I) strategic depot placement, (II) a hybrid obstacle-aware route planner, and (III) a time-window-aware (TWA) Mixed-Integer Linear Programming (MILP) scheduler coupled to a battery/temperature feasibility model. Four global planners—Ant Colony Optimisation (ACO), Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Rapidly Exploring Random Tree* (RRT*)—are paired with lightweight local refiners, Simulated Annealing (SA) and Adaptive Large-Neighbourhood Search (ALNS). Benchmarks over 12 destinations used real Civil Aviation Authority no-fly zones and energy constraints. RRT*-based hybrids delivered the shortest mean paths: RRT* + SA and RRT* + ALNS tied for the best average length, while RRT* + SA also achieved the co-lowest runtime at $v=60\mathrm{km}{\mathrm{h}}^{-1}$. The TWA-MILP reached proven optimality in 0.11 s, showing that a minimum of seven UAVs are required to satisfy all 20–30 min delivery windows in a single wave; a rolling demand of one request every 15 min can be sustained with three UAVs if each sortie (including service/recharge) completes within 45 min. To validate against a state-of-the-art operations-research baseline, we also implemented a Vehicle Routing Problem with Time Windows (VRPTW) in Google OR-Tools, confirming that our hybrid planners generate competitive or shorter NFZ-aware routes in complex corridors. Digital-twin validation in AirborneSIM confirmed CAP 722-compliant, flyable trajectories under wind and sensor noise. By hybridising a fast, probabilistically complete sampler (RRT*) with a sub-second refiner (SA/ALNS) and embedding energy-aware scheduling, the framework offers an actionable blueprint for emergency medical UAV networks. Full article

This study presents the design and experimental evaluation of a 10 MHz voltage regulator module (VRM) that incorporates a solenoid inductor embedded within a printed circuit board (PCB). To verify the performance of the inductor, a test PCB was fabricated and characterized using a vector network analyzer (VNA), with measurement data processed through 2x-thru de-embedding technique. A 10 MHz VRM was then implemented to assess the impact of the embedded inductor on system efficiency. Comparative measurements were conducted between two VRMs—one employing a surface-mounted (SMT) inductor and the other a PCB-embedded inductor. The SMT-based system achieved a peak efficiency of 65.24% at a load current of 800 mA, whereas the PCB-embedded inductor version reached 70.43% at 900 mA, reflecting an improvement of 5.19%. The VRM with an embedded inductor experienced less efficiency degradation under heavy load conditions, demonstrating superior energy delivery stability. These findings confirm the practical benefits of integrating solenoid inductors within a PCB for high-frequency, high-efficiency power conversion. Full article

Kinetoplastids are protozoa that possess a unique organelle called a kinetoplast. These include the parasites Trypanosoma cruzi, T. brucei and related African trypanosomes, and Leishmania spp. These parasites cause a variety of neglected tropical diseases in humans and livestock, with devastating consequences. In the absence of any vaccine, pharmaceutical interventions are the mainstay of control, but these have historically been underfunded, fragmented, and inadequately aligned with the complex zoonotic and ecological realities of the parasites’ transmission dynamics. In this review, the landscape of current and emerging drugs for treating leishmaniasis, Chagas disease, and African trypanosomiasis is critically evaluated across both veterinary and human contexts. It examines the challenges of legacy compounds, the pharmacological shortcomings in multi-host, multi-tropic and multi-stage disease systems, and the gaps in veterinary therapeutics, specifically for African animal trypanosomiasis and canine leishmaniasis but also the animal reservoir of T. cruzi. Emphasis is placed on pharmacokinetic divergence between species, the accompanying risks with the use of off-label human drugs in animals, and the ecological effects of environmental drug exposure. We propose a far-reaching One Health framework for pharmaceutical research and development, promoting dual-indication co-development, ecological pharmacology, regulatory harmonisation, and integrated delivery systems. In this context, we argue that the drug development pipeline must be rationalised as a transdisciplinary and ecologically embedded process, able to interrupt parasite transmission to human, animal, and vector interfaces. Our findings reveal that we can bridge age-old therapeutic gaps, advance towards sustainable control, and eventually eliminate the neglected diseases caused by kinetoplastid protozoan parasites by aligning pharmaceutical innovation with One Health principles. This article aims to promote future research and development of innovative drugs that are sustainable under the One Health framework. Full article

Background: The COVID-19 pandemic disrupted essential health services globally, including contraceptive provision. This study examined barriers to contraceptive access in Nigeria during the national lockdown and lessons for future health crisis preparedness. Methods: A cross-sectional online survey of 1273 respondents was conducted during the COVID-19 lockdown. Descriptive statistics and multivariate logistic regression were used to identify predictors of unmet contraceptive need. Online convenience sampling may limit representativeness. Results: Fear of contracting COVID-19 at health facilities (76.6%), closure of drug and chemist shops (53.7%), movement restrictions (48.4%), and inability to reach healthcare providers (43.5%) were the most reported barriers. Adults aged 26–33 years (AOR = 2.00, 95% CI: 1.05–3.73), those married or cohabiting (AOR = 3.87, 95% CI: 2.58–5.68), and Yoruba respondents (AOR = 1.70, 95% CI: 1.04–2.58) were significantly more likely to report unmet need. Tertiary education (AOR = 0.28, 95% CI: 0.13–0.55) and rural residence (AOR = 0.57, 95% CI: 0.37–0.86) were protective factors. Conclusion: COVID-19-related restrictions exposed systemic weaknesses in Nigeria’s contraceptive delivery. Addressing fragile supply chains, strengthening community-based alternatives, and embedding reproductive health into emergency preparedness plans will be critical to building resilient systems for future crises. Full article

Semantic communication (SemCom), as a revolutionary paradigm for next-generation networks, shifts the focus from traditional bit-level transmission to the delivery of meaning and purpose. Grounded in the Data, Information, Knowledge, Wisdom, Purpose (DIKWP) model and its mapping framework, together with the relativity of understanding theory, the discussion systematically reviews advances in semantic-aware communication and personalized semantic security. By innovatively introducing the “Purpose” dimension atop the classical DIKW hierarchy and establishing interlayer feedback mechanisms, the DIKWP model enables purpose-driven, dynamic semantic processing, providing a theoretical foundation for both SemCom and personalized semantic security based on cognitive differences. A comparative analysis of existing SemCom architectures, personalized artificial intelligence (AI) systems, and secure communication mechanisms highlights the unique value of the DIKWP model. An integrated cognitive–conceptual–semantic network, combined with the principle of semantic relativity, supports the development of explainable, cognitively adaptive, and trustworthy communication systems. Practical implementation paths are explored, including DIKWP-based semantic chip design, white-box AI evaluation standards, and dynamic semantic protection frameworks, establishing theoretical links with emerging trends such as task-oriented communication and personalized foundation models. Embedding knowledge representation and cognitive context into communication protocols is shown to enhance efficiency, reliability, and security significantly. In addition, key research challenges in semantic alignment, cross-domain knowledge sharing, and formal semantic metrics are identified, while future research directions are outlined to guide the evolution of intelligent communication networks and provide a systematic reference for the advancement of the field. Full article

This study presents a pH-responsive drug delivery platform, created based on naproxen-loaded zeolitic imidazolate frameworks (ZIF) and kaolin-ZIF (Kao@ZIF) nanocarriers embedded in a 3D-printed polylactic acid (PLA) scaffold coated with a gelatin hydrogel. The PLA discs were designed as structural tissue models to simulate localized drug release. Kaolin (Kao), a basic mineral in the kaolin group that includes halloysite, was selected as a chemically stable and biocompatible adsorbent to enhance ZIF integrity and system reliability. To address the concerns about the safety and reproducibility of nanoscale materials in biomedical applications, structurally stable ZIF and Kao@ZIF nanocarriers were synthesized and characterized using FT-IR, SEM-EDS, and LC-M/MS, measuring drug loading efficiencies over 90% for ZIF and slightly higher for Kao@ZIF. In vitro release profiles showed strong pH sensitivity, with greater naproxen release at acidic pH (5.4) and more sustained release from Kao@ZIF. Cytotoxicity assays using L929 fibroblasts demonstrated improved biocompatibility, with cell viabilities of approximately 75% for ZIF–naproxen, 82% for Kao@ZIF–naproxen, and 90% for gelatin-coated PLA–Kao@ZIF scaffolds, for 24 h incubation. Incorporating kaolin-stabilized ZIF nanocarriers into 3D-printed biodegradable scaffolds offers a promising and safer approach for pH-sensitive, tissue-targeted drug delivery, while laying the groundwork for future studies involving halloysite-derived nanotubular systems. Full article

Classification of video traffic is crucial for network management, enforcing quality of service, and optimising bandwidth. Feature selection plays a vital role in traffic identification by reducing data volume, enhancing accuracy, and reducing computational cost. This paper presents a comparative study of three feature selection approaches applied to video traffic identification: filter, wrapper, and embedded. Real-world traffic traces are collected from three popular video streaming platforms: YouTube, Netflix, and Amazon Prime Video, representing diverse content delivery characteristics. The main contributions of this work are (1) the identification of traffic generated by these streaming services, (2) a comparative evaluation of three feature selection methods, and (3) the application of previously untested algorithms for this task. We evaluate the examined methods using F1-score and computational efficiency. The results demonstrate distinct trade-offs among the approaches: the filter method offers low computational overhead with moderate accuracy, while the wrapper method achieves higher accuracy at the cost of longer processing times. The embedded method provides a balanced compromise by integrating feature selection within model training. This comparative analysis offers insights for designing video traffic identification systems in modern heterogeneous networks. Full article