Search Results (7,746)

Memristors, as the fourth fundamental circuit element, have attracted significant interest for their potential in analog signal processing, computing, and memory storage technologies. However, physical memristor implementations still face challenges in reproducibility, scalability, and integration with standard CMOS processes. Memristor emulator circuits, implemented using analog, digital, and mixed components, have emerged as practical alternatives, offering tunability, cost effectiveness, and compatibility with existing fabrication technologies for research and prototyping. This review paper provides a comprehensive analysis of recent advancements in memristor emulator design methodologies, including active and passive analog circuits, digital implementations, and hybrid approaches. A critical evaluation of these emulation techniques is conducted based on several performance metrics, including maximum operational frequency range, power consumption, and circuit topology. Additional parameters are also taken into account to ensure a comprehensive assessment. Furthermore, the paper examines promising healthcare applications of memristor and memristor emulators, focusing on their integration into biomedical systems. Finally, key challenges and promising directions for future research in memristor emulator development are outlined. Overall, the research presented highlights the promising future of memristor emulator technology in bridging the gap between theoretical memristor models and practical circuit implementations. Full article

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract, primarily driven by activating mutations in KIT (CD117) and platelet-derived growth factor receptor alpha (PDGFRA). The introduction of tyrosine kinase inhibitors (TKIs), especially imatinib, has significantly transformed GIST treatment. However, the emergence of both primary and secondary resistance to imatinib presents ongoing therapeutic challenges. This review comprehensively explores the mechanisms underlying imatinib resistance and evaluates subsequent TKI therapies. Sunitinib, regorafenib, and ripretinib are currently approved as standard second-, third-, and fourth-line therapies, each demonstrating efficacy against distinct mutational profiles. Avapritinib, notably effective against PDGFRA D842V mutations, represents a milestone for previously untreatable subgroups. Several alternative agents—such as nilotinib, masitinib, sorafenib, dovitinib, pazopanib, and ponatinib—have shown varying degrees of success in refractory cases or specific genotypes. Investigational compounds, including crenolanib, bezuclastinib, famitinib, motesanib, midostaurin, IDRX-42, and olverembatinib, are under development to address resistant or wild-type GISTs. Despite progress, long-term efficacy remains limited due to evolving resistance. Future strategies include precision medicine approaches such as ctDNA-guided therapy, rational drug combinations, and novel drug delivery systems to optimize bioavailability and reduce toxicity. Ongoing research will be crucial for refining treatment sequencing and expanding therapeutic options, especially for rare GIST subtypes. Full article

Background/Objectives: The work presented herein focused on the development and characterization of a transdermal caffeine platform fabricated from ultrathin micro- and submicron fibers produced via electrospinning. Methods: The formulations incorporated caffeine encapsulated in a polyethylene oxide (PEO) matrix, combined with various permeation enhancers. A backing layer made of annealed electrospun polycaprolactone (PCL) facilitated the lamination of the two layers to form the final multilayer patch. Comprehensive characterization was conducted, utilizing scanning electron microscopy (SEM) to assess the fiber morphology, attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) for chemical detection and to assess the stability of the caffeine, and differential scanning calorimetry (DSC) along with wide-angle X-ray scattering (WAXS) to analyze the physical state of the caffeine within the fibers of the active layer. Additionally, Franz cell permeation studies were performed using both synthetic membranes (Strat-M) and ex vivo human stratum corneum (SC) to evaluate and model the permeation kinetics. Results: These experiments demonstrated the significant role of enhancers in modulating the caffeine permeation rates provided by the patch, achieving permeation rates of up to 0.73 mg/cm² within 24 h. Conclusions: This work highlights the potential of using electro-hydrodynamic processing technology to develop innovative transdermal delivery systems for drugs, offering a promising strategy for enhancing efficacy and innovative therapeutic direct plasma administration. Full article

Food quality and safety are essential for ensuring public health, preventing foodborne illness, reducing food waste, maintaining consumer confidence, and supporting regulatory compliance and international trade. This has led to the emergence of many research works that focus on automating and streamlining the assessment of food quality. Electronic noses have become of paramount importance in this context. We analyze the current state of research in the development of electronic noses for food quality and safety. We examined research papers published in three different scientific databases in the last decade, leading to a comprehensive review of the field. Our review found that most of the efforts use portable, low-cost electronic noses, coupled with pattern recognition algorithms, for evaluating the quality levels in certain well-defined food classes, reaching accuracies exceeding 90% in most cases. Despite these encouraging results, key challenges remain, particularly in diversifying the sensor response across complex substances, improving odor differentiation, compensating for sensor drift, and ensuring real-world reliability. These limitations indicate that a complete device mimicking the flexibility and selectivity of the human olfactory system is not yet available. To address these gaps, our review recommends solutions such as the adoption of adaptive machine learning models to reduce calibration needs and enhance drift resilience and the implementation of standardized protocols for data acquisition and model validation. We introduce benchmark comparisons and a future roadmap for electronic noses that demonstrate their potential to evolve from controlled studies to scalable industrial applications. In doing so, this review aims not only to assess the state of the field but also to support its transition toward more robust, interpretable, and field-ready electronic nose technologies. Full article

Introduction: Assessing the quality of patient care within healthcare systems remains a multifaceted challenge due to varying definitions of “quality” and the complexity of care delivery structures worldwide. Patient-centeredness, institutional responsiveness, and contextual adaptability are increasingly recognized as core pillars in quality assessment. Objective: This narrative literature review aims to explore conceptual models and practical frameworks for evaluating healthcare quality, emphasizing tools that integrate technical, functional, and emotional dimensions and proposing a comprehensive model adaptable to diverse health system contexts. Methodology: A systematic literature search was conducted in the PubMed, Scopus, and Cochrane Library databases, covering the years 2000 to 2024. Studies were selected based on relevance to quality assessment models, patient satisfaction, accreditation, and strategic improvement methodologies. The review followed a thematic synthesis approach, integrating structural, process-based, and outcome-driven perspectives. Results: Core frameworks such as Donabedian’s model and balancing measures were reviewed alongside evaluation tools like the Dutch Consumer Quality Index, SERVQUAL, and Importance–Performance Analysis (IPA). These models revealed significant gaps between patient expectations and actual service delivery, especially in functional and emotional quality dimensions. This review also identified limitations related to contextual generalizability and bias. A novel integrative model is proposed, emphasizing the dynamic interaction between institutional structure, clinical processes, and patient experience. Conclusions: High-quality healthcare demands a multidimensional approach. Integrating conceptual frameworks with context-sensitive strategies enables healthcare systems to align technical performance with patient-centered outcomes. The proposed model offers a foundation for future empirical validation, particularly in resource-limited or hybrid settings. Full article

Air quality monitoring systems based on Internet of Things (IoT) technology are critical for addressing environmental and public health challenges, but their energy efficiency poses a significant challenge to their autonomous and scalable deployment. This study investigates strategies to enhance the energy efficiency of IoT-based air quality monitoring systems. A comprehensive analysis of sensor types, data transmission protocols, and system architectures was conducted, focusing on their energy consumption. An energy-efficient system was designed using the Smart Air sensor, Zigbee gateway, and Mini UPS, with its performance evaluated through daily energy consumption, backup operation time, and annual energy use. An integrated efficiency index (IEI) was introduced to compare sensor models based on functionality, energy efficiency, and cost. The proposed system achieves a daily energy consumption of 72 W·h, supports up to 10 h of autonomous operation during outages, and consumes 26.28 kW·h annually. The IEI analysis identified the Ajax LifeQuality as the most energy-efficient sensor, while Smart Air offers a cost-effective alternative with broader functionality. The proposed architecture and IEI provide a scalable and sustainable framework for IoT air quality monitoring, with potential applications in smart cities and residential settings. Future research should explore renewable energy integration and predictive energy management. Full article

The ability to collaborate with new teammates, adapt to unfamiliar environments, and engage in effective planning is essential for multi-drone agents within unmanned combat systems. This paper introduces DETEAMSK (Model-based Reinforcement Learning by Decoupling the Identification of Teammates and Tasks), a model-based reinforcement learning method in intelligent top-level planning and decisions designed for ad hoc teamwork among multi-drone agents. It specifically addresses integrated reconnaissance and strike missions in urban combat scenarios under varying conditions. DETEAMSK’s performance is evaluated through comprehensive, multidimensional experiments and compared with other baseline models. The results demonstrate that DETEAMSK exhibits superior effectiveness, robustness, and generalization capabilities across a range of task domains. Moreover, the model-based reinforcement learning approach offers distinct advantages over traditional models, such as the PLASTIC-Model, and model-free approaches, like the PLASTIC-Policy, due to its unique “dynamic decoupling identification” feature. This study provides valuable insights for advancing both theoretical and applied research in model-based reinforcement learning methods for multi-drone systems. Full article

Water scarcity, exacerbated by climate change, population growth, and competing sectoral demands, poses a major threat to agricultural sustainability, particularly in irrigated regions such as the Nile Delta in Egypt. Addressing this challenge requires innovative approaches to evaluate irrigation performance despite the limitations in ground data availability. Traditional assessment methods are often costly, labor-intensive, and reliant on field data, limiting their scalability, especially in data-scarce regions. This paper addresses this gap by presenting a comprehensive and scalable framework that employs publicly accessible satellite data to map crop types and subsequently assess irrigation performance without the need for ground truthing. The framework consists of two parts: First, crop mapping, which was conducted seasonally between 2015 and 2020 for the four primary crops in the Nile Delta (rice, maize, wheat, and clover). The WaPOR v2 Land Cover Classification layer was used as a substitute for ground truth data to label the Landsat-8 images for training the random forest algorithm. The crop maps generated at 30 m resolution had moderate to high accuracy, with overall accuracy ranging from 0.77 to 0.80 in summer and 0.87–0.95 in winter. The estimated crop areas aligned well with national agricultural statistics. Second, based on the mapped crops, three irrigation performance indicators—adequacy, reliability, and equity—were calculated and compared with their established standards. The results reveal a good level of equity, with values consistently below 10%, and a relatively reliable water supply, as indicated by the reliability indicator (0.02–0.08). Average summer adequacy ranged from 0.4 to 0.63, indicating insufficient supply, whereas winter values (1.3 to 1.7) reflected a surplus. A noticeable improvement gradient was observed for all indicators toward the north of the delta, while areas located in the delta’s new lands consistently displayed unfavorable conditions in all indicators. This approach facilitates the identification of regions where agricultural performance falls short of its potential, thereby offering valuable insights into where and how irrigation systems can be strategically improved to enhance overall performance sustainably. Full article

Emerging digital technologies are transforming how consumers participate in financial markets, yet their benefits depend critically on the speed, reliability, and transparency of the underlying platforms. Online stock trading platforms must maintain high efficiency underload to ensure a good user experience. This paper presents performance analysis under various load conditions based on the containerized stock exchange system. A comprehensive data logging pipeline was implemented, capturing metrics such as API response times, database query times, and resource utilization. We analyze the collected data to identify performance patterns, using both statistical analysis and machine learning techniques. Preliminary analysis reveals correlations between application processing time and database load, as well as the impact of user behavior on system performance. Association rule mining is applied to uncover relationships among performance metrics, and multiple classification algorithms are evaluated for their ability to predict user activity class patterns from system metrics. The insights from this work can guide optimizations in similar distributed web applications to improve scalability and reliability under a heavy load. By framing performance not merely as a technical property but as a determinant of financial decision-making and well-being, the study contributes actionable insights for designers of consumer-facing fintech services seeking to meet sustainable development goals through trustworthy, resilient digital infrastructure. Full article

Enhancing the resilience of the oil and gas industry chain is essential for achieving sustainable energy development amid global industrial restructuring and the accelerating low-carbon transformation. This study identifies the core contradictions in the development of China’s OGI and constructs a comprehensive evaluation index system to assess the resilience of the industry from the four sustainability-aligned dimensions of resistance, recovery, innovation, and transformation. Using the entropy weight comprehensive evaluation model, obstacle degree model, and coupling coordination degree model, the resilience performance of China’s OGI chain is evaluated from 2001 to 2022. The results show a significant upward trend in overall resilience, with evident stage characteristics. Resistance remains relatively stable, recovery shows the most improvement, innovation steadily increases, and transformation accelerates after 2019, particularly in response to China’s dual carbon goals. Key barriers include limited CCUS deployment and insufficient downstream innovation capacity. The improved coupling coordination among resilience subsystems highlights enhanced systemic synergy. These findings offer valuable implications for strengthening the sustainability and security of energy supply chains under climate and geopolitical pressures. Full article

As the global demand for eco-friendly food ingredients grows, marine macroalgae emerge as a valuable resource for multiple applications using a circular bioeconomy approach. In this study, green macroalgae Ulva flexuosa, naturally accumulated in aquaculture ponds as a residual biomass (by-product) of shrimp and oyster farming, were investigated regarding their bioactivity, chemical composition, and antioxidant properties. The use of aquaculture by-products as raw materials not only reduces waste accumulation but also makes better use of natural resources and adds value to underutilized biomass, contributing to sustainable production systems. For this, a comprehensive approach including the evaluation of its composition and environmentally friendly extraction of bioactive compounds was conducted and discussed. Green macroalgae exhibited high fiber (37.63% dry weight, DW) and mineral (30.45% DW) contents. Among the identified compounds, palmitic acid and linoleic acid (ω-6) were identified in the highest concentrations. Pigment analysis revealed a high concentration of chlorophylls (73.95 mg/g) and carotenoids (17.75 mg/g). To evaluate the bioactivity of Ulva flexuosa, ultrasound-assisted solid–liquid extraction was performed using water, ethanol, and methanol. Methanolic extracts showed the highest flavonoid content (59.33 mg QE/100 g), while aqueous extracts had the highest total phenolic content (41.50 mg GAE/100 g). Ethanolic and methanolic extracts had the most potent DPPH scavenging activity, whereas aqueous and ethanolic extracts performed best at the ABTS assay. Overall, we show the upcycling of Ulva flexuosa, an underexplored aquaculture by-product, as a sustainable and sensible strategy for multiple value-added applications. Full article

The value realization of forest ecological products (VRF) is crucial for rural revitalization, while the rural digital economy (RDE) plays a central role in enhancing farmers’ income (FI). This study constructs index systems to evaluate the RDE and VRF using the entropy weight method and the input–output model. Based on panel data from 31 Chinese provinces (2011–2021), we employ a comprehensive analytical framework that includes spatiotemporal evolution analysis, benchmark regression models, mediation effect analysis, and heterogeneity analysis. The results of the benchmark regression models show that the RDE significantly boosts FI, with each unit of increase in the RDE leading to a 2579-unit rise in income. Spatiotemporal evolution analysis reveals that the positive effect of the RDE weakens from the Eastern coastal regions to the less developed Western regions. Furthermore, mediation effect analysis indicates that VRF mediates the relationship between the RDE and FI. Heterogeneity analysis demonstrates that the impact of the RDE varies across regions and income levels. These findings provide strong evidence of the role of the RDE in promoting FI and highlight VRF as a mediating mechanism, offering policy insights for integrating digital and ecological strategies to foster inclusive rural growth. Full article

This study explores modernization strategies for existing district heating (DH) networks to enhance their efficiency and sustainability, focusing on achieving net-zero emissions in urban heating systems. Building upon a literature review and expert interviews, we developed a strategic decision-support framework that outlines distinct strategies for retrofitting district heating grids and includes a portfolio analysis. This framework serves as a tool to guide DH operators and stakeholders in selecting well-founded modernization pathways by considering technical, economic, and social dimensions. The review identifies several promising measures, such as reducing operational temperatures at substations, implementing optimized substations, integrating renewable and waste heat sources, implementing thermal energy storage (TES), deploying smart metering and monitoring infrastructure, and expanding networks while addressing public concerns. Additionally, the review highlights the importance of stakeholder engagement and policy support in successfully implementing these strategies. The developed strategic decision-support framework helps practitioners select a tailored modernization strategy aligned with the local context. Furthermore, the findings show the necessity of adopting a comprehensive approach that combines technical upgrades with robust stakeholder involvement and supportive policy measures to facilitate the transition to sustainable urban heating solutions. For example, the development of decision-support tools enables stakeholders to systematically evaluate and select grid modernization strategies, directly helping to reduce transmission losses and lower greenhouse gas (GHG) emissions contributing to climate goals and enhancing energy security. Indeed, as shown in the reviewed literature, retrofitting high-temperature district heating networks with low-temperature distribution and integrating renewables can lead to near-complete decarbonization of the supplied heat. Additionally, integrating advanced digital technologies, such as smart grid systems, can enhance grid efficiency and enable a greater share of variable renewable energy thus supporting national decarbonization targets. Further investigation could point to the most determining context factors for best choices to improve the sustainability and efficiency of existing DH systems. Full article

Polyethylene terephthalate (PET) pollution represents a significant environmental challenge due to its widespread use and recalcitrant nature. PET-degrading enzymes, particularly Ideonella sakaiensis PETases (IsPETase), have emerged as promising biocatalysts for mitigating this problem. This review provides a comprehensive overview of recent advancements in the discovery and heterologous expression of IsPETase and closely related enzymes. We highlight innovative approaches, such as in silico and AI-based enzyme screening and advanced screening assays. Strategies to enhance enzyme secretion and solubility, such as using signal peptides, fusion tags, chaperone co-expression, cell surface display systems, and membrane permeability modulation, are critically evaluated. Despite considerable progress, challenges remain in achieving industrial-scale production and application. Future research must focus on integrating cutting-edge molecular biology techniques with host-specific optimisation to achieve sustainable and cost-effective solutions for PET biodegradation and recycling. This review aims to provide a foundation for further exploration and innovation in the field of enzymatic plastic degradation. Full article

Drought stress has a significant impact on cotton growth, development, and productivity. This study conducted drought stress treatment and normal water treatment (control group) on 502 cotton accessions and analyzed data on eight phenotypic traits closely related to drought stress tolerance. The results showed that all indicators changed significantly under drought stress conditions compared to the control group, with varying degrees of response among different indicators. To comprehensively evaluate the drought resistance of cotton during the germination period, the values of drought resistance comprehensive evaluation (D-value), weight drought resistance coefficient (WDC-value), and comprehensive drought resistance coefficient (CDC-value) were calculated based on membership function analysis and principal component analysis. Cluster analysis based on the D-value divided the germplasm into five drought-resistant grades, followed by the selection of one extreme material, each from the strongly drought-resistant and strongly drought-sensitive groups. An evaluation model was established using stepwise regression analysis, including the following effective indicators: Relative Fresh Weight (RFW), Relative Hypocotyl Length (RHL), Relative Seeds Water Absorption Rate (RAR), Relative Germination Rate (RGR), Relative Germination Potential (RGP), and Relative Drought Tolerance Index (RDT). The validation of the D-value prediction model based on the Best Linear Unbiased Prediction (BLUP) showed that the results obtained from two independent biological replicates were highly consistent. The comprehensive evaluation system and screening indicators established in this study provide a reliable method for identifying drought tolerance during the germination period. Full article